{"title":"Expert systems \/ knowledge-based systems Books","description":"","products":[{"product_id":"practical-data-science-9781484230534","title":"Practical Data Science","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003e\u003cdiv\u003eLearn how to build a data science technology stack and perform good data science with repeatable methods. You will learn how to turn data lakes into business assets.\u003c\/div\u003e\u003cdiv\u003e\u003cbr\u003e\u003c\/div\u003e\u003cdiv\u003eThe data science technology stack demonstrated in \u003ci\u003ePractical Data Science \u003c\/i\u003eis built from components in general use in the industry. Data scientist Andreas Vermeulen demonstrates in detail how to build and provision a technology stack to yield repeatable results. He shows you how to apply practical methods to extract actionable business knowledge from data lakes consisting of data from a polyglot of data types and dimensions.\u003cbr\u003e\n\u003c\/div\u003e\u003cdiv\u003e\u003cbr\u003e\u003c\/div\u003e\u003cdiv\u003e\u003cb\u003eWhat You''ll Learn\u003c\/b\u003e\u003c\/div\u003e\u003cdiv\u003e\u003cdiv\u003e\u003cul\u003e\n\u003cli\u003eBecome fluent in the essential concepts and terminology of data science and data engineering \u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eBuild and use a technology stack that meets industry criteria\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eMaster the methods for retrieving actionable business knowledge\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eCoordinate the handling of\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003e\n\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\u003c\/div\u003e\u003c\/div\u003e","brand":"APress","offers":[{"title":"Default Title","offer_id":48739662856535,"sku":"9781484230534","price":999.99,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781484230534.jpg?v=1720052848"},{"product_id":"modern-data-engineering-with-apache-spark-9781484274514","title":"Modern Data Engineering with Apache Spark","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003eLeverage Apache Spark within a modern data engineering ecosystem.This hands-on guide will teach you how to write fully functional applications, follow industry best practices, and learn the rationale behind these decisions. With Apache Spark as the foundation, you will follow a step-by-step journey beginning with the basics of data ingestion, processing, and transformation, and ending up with an entire local data platform running Apache Spark, Apache Zeppelin, Apache Kafka, Redis, MySQL, Minio (S3), and Apache Airflow.   Apache Spark applications solve a wide range of data problems from traditional data loading and processing to rich SQL-based analysis as well as complex machine learning workloads and even near real-time processing of streaming data. Spark fits well as a central foundation for any data engineering workload. This book will teach you to write interactive Spark applications using Apache Zeppelin notebooks, write and compilereusable applications and modules, and fully test\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003e\u003cb\u003e​Part I. The Fundamentals of Data Engineering with Spark\u003c\/b\u003e1. Introduction to Modern Data Engineering2. Getting Started with Apache Spark3. Working with Data4. Transforming Data with Spark SQL and the  DataFrame API5. Bridging Spark SQL with JDBC6. Data Discovery and the Spark SQL Catalog7. Data Pipelines \u0026amp; Structured Spark Applications\u003cbr\u003e\u003cb\u003ePart II. The Streaming Pipeline Ecosystem\u003c\/b\u003e8. Workflow Orchestration with Apache Airflow9. A Gentle Introduction to Stream Processing10. Patterns for Writing Structured Streaming Applications11. Apache Kafka \u0026amp; Spark Structured Streaming12. Analytical Processing \u0026amp; Insights\u003cbr\u003e\u003cb\u003ePart III. Advanced Techniques\u003c\/b\u003e13. Advanced Analytics with Spark Stateful Structured Streaming14. Deploying Mission Critical Spark Applications on Spark Standalone15. Deploying Mission Critical Spark Applications on Kubernetes","brand":"APress","offers":[{"title":"Default Title","offer_id":48739667083607,"sku":"9781484274514","price":52.24,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781484274514.jpg?v=1791472331"},{"product_id":"data-science-and-analytics-for-smes-9781484286692","title":"Data Science and Analytics for SMEs","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003eMaster the tricks and techniques of business analytics consulting, specifically applicable to small-to-medium businesses (SMEs). Written to help you hone your business analytics skills, this book applies data science techniques to help solve problems and improve upon many aspects of a business'' operations. \u003cdiv\u003e\u003cbr\u003e\u003c\/div\u003e\u003cdiv\u003eSMEs are looking for ways to use data science and analytics, and this need is becoming increasingly pressing with the ongoing digital revolution. The topics covered in the books will help to provide the knowledge leverage needed for implementing data science in small business. The demand of small business for data analytics are in conjunction with the growing number of freelance data science consulting opportunities; hence this book will provide insight on how to navigate this new terrain.\u003cdiv\u003e\u003cbr\u003e\u003c\/div\u003e\n\u003cdiv\u003eThis book uses a do-it-yourself approach to analytics and introduces tools that are easily available online and are non-programming based. Data science\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTrade Review\u003c\/h2\u003e“By reading the book and working out the use case, subject matter experts will be able to get a coherent roadmap to the main techniques available for both descriptive and predictive data analytics, as well as be able to provide simple services related to their company data and future prospects.” (Rosario Uceda-Sosa, Computing Reviews, October 2, 2023)\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003e​ INTRODUCTION\u003cbr\u003eWe introduce data science generally and narrow it down to data science for business which is also referred to as business analytics. We then give a detailed explanation of the process involved in business analytics in form of the business analytics journey. In this journey, we explain what it takes from start to finish to carry out an analytics project in the business world, focusing on small business consulting, even though the process is generic to all types of business, small or large. We also give a description of what small business refers to in this book and the peculiarities of navigating an analytics project in such a terrain.  To conclude the chapter, we talk about the types of analytics problems that is common to small business and the tools available to solve these problems given the budget situation of small businesses when it comes to analytics project.\u003cbr\u003e·         DATA SCIENCE\u003cbr\u003e·         DATA SCIENCE FOR BUSINESS\u003cbr\u003e·         BUSINESS ANALYTICS JOURNEY\u003cbr\u003e·         SMALL AND MEDIUM BUSINESS (SME)\u003cbr\u003e·         BUSINESS ANALYTICS IN SMALL BUSINESS\u003cbr\u003e·         TYPES OF ANALYTICS PROBLEMS IN SME\u003cbr\u003e·         ANALYTICS TOOLS FOR SMES\u003cbr\u003e·         ROAD MAPS TO THIS BOOK\u003cbr\u003e·         PROBLEMS\u003cbr\u003e·         REFERENCES\u003cbr\u003e \u003cbr\u003eCHAPTER 1: DATA FOR ANALYSIS IN SMALL BUSINESS\u003cbr\u003eIn this chapter, we would look at the various sources of data generally and in small business. This chapter is important because the major challenge of consulting for small business is the lack of data or quality data for analysis. This chapter will therefore detail the sources of data for analysis explaining first the type or form that data exists and some general ideas of how to collect such data. It gives an overview on data quality and integrity issues and touches on data literacy. The chapter also includes the typical data preparation procedures for the common types of techniques used in small business analytics and by extension used in this book. To conclude the chapter, we look at data visualization, particularly towards preparing data for various analytics task as explained in section 1.3.\u003cbr\u003e·         SOURCE OF DATA\u003cbr\u003e·         DATA QUALITY \u0026amp; INTEGRITY\u003cbr\u003e·         DATA GOVERNANCE\u003cbr\u003e·         DATA PREPARATION\u003cbr\u003e·         DATA VISUALIZATION\u003cbr\u003e·         PROBLEMS\u003cbr\u003e·         REFERENCES\u003cbr\u003eCHAPTER 2: BUSINESS ANALYTICS CONSULTING\u003cbr\u003eIn this chapter, we will look at business analytics consulting, particularly what the concept implies and how to build such a career path. We will explain the types of business analytics consulting that exist and then narrow it down to how to navigate the world of business analytics consulting for small business. In this chapter, we will look at how to manage a typical analytics project and measure the success of analytics projects. In conclusion, we will discuss issues revolving around how to bill analytics project particularly as a consultant.\u003cbr\u003e·         BUSINESS ANALYTICS CONSULTING\u003cbr\u003e·         MANAGING ANALYTICS PROJECT\u003cbr\u003e·         SUCCESS METRICS IN ANALYTICS PROJECT\u003cbr\u003e·         BILLING ANALYTICS PROJECT\u003cbr\u003e·         PROBLEMS\u003cbr\u003e·         REFERENCES\u003cbr\u003eCHAPTER 3: BUSINESS ANALYTICS CONSULTING PHASES\u003cbr\u003eIn this chapter we will look at the stages involved business analytics consulting, particularly when the analytics service is offered as a product from either within or outside the business. We will look at the proposal and initial analysis stage which gives direction to the analytics project. Then we look at the details involved in the pre-engagement, engagement and post engagement phase. It is important to know that the stages are presented in a typical or generic way but when implemented, there might be reason to modify or customize them for the application scenario.\u003cbr\u003e·         PROPOSAL \u0026amp; INITIAL ANALYSIS\u003cbr\u003e·         PRE- ENGAGEMENT PHASE\u003cbr\u003e·         ENGAGEMENT PHASE\u003cbr\u003e·         POST ENGAGEMENT PHASE\u003cbr\u003e·         PROBLEMS\u003cbr\u003e·         REFERENCES\u003cbr\u003e \u003cbr\u003eCHAPTER 4: DESCRIPTIVE ANALYTICS TOOLS\u003cbr\u003eThis chapter is focused on the mostly common descriptive analytics tools used in business generally and specifically in small businesses. The chapter will help  to use descriptive analytics tools to understand your business and make recommendations that can improve your business profits. For small business, descriptive analytics helps SMEs to make sense of available data in order to monitor business indicators at a glance, helps SME owners to observe sales trends and patterns on an overall basis, as well as deep-dive into product categories and customer groups.  It also helps SME’s to plan product strategy, pricing policies that will maximize their projected revenues and derive a lot of valuable insights for getting more customers.\u003cbr\u003e \u003cbr\u003e·         INTRODUCTION\u003cbr\u003e·         BAR CHART\u003cbr\u003e·         HISTOGRAM\u003cbr\u003e·         LINE GRAPHS\u003cbr\u003e·         SCATTER PLOTS\u003cbr\u003e·         PACKED BUBBLES CHARTS\u003cbr\u003e·         HEAT MAPS\u003cbr\u003e·         GEOGRAPHICAL MAPS\u003cbr\u003e·         A PRACTICAL BUSINESS PROBLEM I\u003cbr\u003e·         PROBLEMS\u003cbr\u003e·         REFERENCES\u003cbr\u003e \u003cbr\u003eCHAPTER 5: PREDICTION TECHNIQUES\u003cbr\u003eIn this chapter, we will explore the popular techniques used for prediction, particularly in retails business. The approach used in explaining these techniques us to use them in solving a business problem. The second business problem to be addressed is the sales prediction problem which is common in retail business. The chapter first explain the fundamental concept of prediction techniques, next we look at how such techniques are evaluated. After this, we describe the business problem we intend solving. We then pick each of the selected techniques one by one and explain the algorithms involved and how they can be used to solve the problem described. The prediction techniques used and compared are the Multiple linear regression, the Regression Trees and the Neural Network. To conclude the chapter, we compare the results of the three algorithms and conclude on the problem in question.  In this chapter therefore, the analytics products being offered is to solve sales prediction problem for small retail business.\u003cbr\u003e·         INTRODUCTION\u003cbr\u003e·         PRACTICAL BUSINESS PROBLEM II (SALES PREDICTION)\u003cbr\u003e·         MULTIPLE LINEAR REGRESSION\u003cbr\u003e·         REGRESSIN TREES\u003cbr\u003e·         NEURAL NETWORK (PREDICTION)\u003cbr\u003e·         CONCLUSION ON SALES PREDICTION\u003cbr\u003e·         PROBLEMS\u003cbr\u003e·         REFERENCES\u003cbr\u003e \u003cbr\u003eCHAPTER 6: CLASSIFICATION TECHNIQUES\u003cbr\u003eIn this chapter, even though there are several classification techniques, we will explore the popular ones used for classification in the business domain.  In doing this, we will use the third business problem centered on customer loyalty comparing neural network, classification tree and random forest algorithms. In solving this problem, we are particular about how to get and retain more customers for our small business.  We will also introduce some other classification based techniques such as K-nearest neighbour logistic regression and persuasion modelling.  We will use persuasion modelling for the fourth practical business problem. In using these techniques to solve the problem we explain the fundamental concepts in the chosen algorithms and use them to demonstrate how this problems solving process can be adopted in real business scenarios.\u003cbr\u003e·         CLASSIFICATION MODELS \u0026amp; EVALUATION\u003cbr\u003e·         PRACTICAL BUSINESS PROBLEM III (CUSTOMER LOYALTY)\u003cbr\u003e·         NEURAL NETWORK\u003cbr\u003e·         CLASSIFICATION TREE\u003cbr\u003e·         RANDOM FOREST \u0026amp; BOOSTED TREES\u003cbr\u003e·         K NEAREST NEIGHBOUR\u003cbr\u003e·         LOGISTIC REGRESSION\u003cbr\u003e·         PROBLEMS\u003cbr\u003e·         REFERENCES\u003cbr\u003e \u003cbr\u003eCHAPTER 7: ADVANCED DESCRIPTIVE ANALYTICS\u003cbr\u003eThis chapter is focused mainly on advanced descriptive analytics techniques. In this chapter, we will first explain the concept of clustering which is a type of unsupervised learning approach. We will then pick one clustering technique which is the K means clustering. Using the fourth practical business problem, we will explain how we can use the K means clustering technique to solve a real business problem. Next will explain the association rule example and finally Network analysis. We conclude with the fifth business problem which is focused on using network analytics for employee efficiency.\u003cbr\u003e·         CLUSTERING\u003cbr\u003e·         K MEANS\u003cbr\u003e·         PRACTICAL BUSINESS PROBLEM IV (Customer Segmentation)\u003cbr\u003e·         ASSOCIATION ANALYSIS\u003cbr\u003e·         NETWORK ANALYSIS\u003cbr\u003e·         PRACTICAL BUSINESS PROBLEM V (Staff Efficiency)\u003cbr\u003e·         PROBLEMS\u003cbr\u003e·         REFERENCES\u003cbr\u003e \u003cbr\u003eCHAPTER 8: CASE STUDY PART I\u003cbr\u003eThis chapter is the beginning part of major consulting case study for this book. We will explain what transpired during a typical business analytics consulting and help to create a road map or an example of how to navigate a business analytics consulting project. We start with a description of the SME Ecommerce environment generally, since this is the business environment of our selected case study, we then talk about the sources of data for analytics peculiar this environment. Next we describe the business to be used as case study briefly, followed by the analytics road map peculiar to consulting for this business. This chapter ends with the results of the initial analysis and pre engagement phase which forms the bases for the detailed analytics and implementation phase in chapter 10.\u003cbr\u003e·         SME ECORMERCE\u003cbr\u003e·         INTRODUCTION TO SME CASE STUDY\u003cbr\u003e·         INITIAL ANALYSIS\u003cbr\u003e·         ANALYTICS APPROACH           \u003cbr\u003e·         PRE –ENGAGEMENT\u003cbr\u003e·         PROBLEMS\u003cbr\u003e·         REFERENCES\u003cbr\u003e \u003cbr\u003eCHAPTER 9: CASE STUDY PART II\u003cbr\u003eIn this chapter, we will conclude the case study used for illustration of a typical business analytics consulting for an SME by presenting the details of the engagement phase for the case study in question. The post engagement phase is left  out as the implementation of the recommendations is determined by the systems and procedures of the business. It is important to note that the consulting steps can be customized for any small business based on the intended problem. The whole steps described in chapter 9 and 10 have been made simple for understanding, though in real life business application there might be need to iterate the process until satisfactory results have been gotten. This is because you constantly need to incorporate feedback from the stakeholders and domain experts.\u003cbr\u003e·         GOAL 1:        INCREASE WEBSITE TRAFFIC\u003cbr\u003e·         GOAL 2:       INCREASE WEBSITE SALES REVENUE\u003cbr\u003e·         PROBLEMS\u003cbr\u003e·         REFERENCES\u003c\/div\u003e\n\u003c\/div\u003e","brand":"APress","offers":[{"title":"Default Title","offer_id":48739668197719,"sku":"9781484286692","price":999.99,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781484286692.jpg?v=1720052860"},{"product_id":"google-cloud-platform-for-data-science-9781484296875","title":"Google Cloud Platform for Data Science","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003eThis book is your practical and comprehensive guide to learning Google Cloud Platform (GCP) for data science, using only the free tier services offered by the platform.   Data science and machine learning are increasingly becoming critical to businesses of all sizes, and the cloud provides a powerful platform for these applications. GCP offers a range of data science services that can be used to store, process, and analyze large datasets, and train and deploy machine learning models.   The book is organized into seven chapters covering various topics such as GCP account setup, Google Colaboratory, Big Data and Machine Learning, Data Visualization and Business Intelligence, Data Processing and Transformation, Data Analytics and Storage, and Advanced Topics. Each chapter provides step-by-step instructions and examples illustrating how to use GCP services for data science and big data projects.   Readers will learn how to set up a Google Colaboratory account and run Jupyternotebooks, access GCP services and data from Colaboratory, use BigQuery for data analytics, and deploy machine learning models using Vertex AI. The book also covers how to visualize data using Looker Data Studio, run data processing pipelines using Google Cloud Dataflow and Dataprep, and store data using Google Cloud Storage and SQL.   What You Will LearnSet up a GCP account and projectExplore BigQuery and its use cases, including machine learningUnderstand Google Cloud AI Platform and its capabilities Use Vertex AI for training and deploying machine learning modelsExplore Google Cloud Dataproc and its use cases for big data processingCreate and share data visualizations and reports with Looker Data StudioExplore Google Cloud Dataflow and its use cases for batch and stream data processing Run data processing pipelines on Cloud DataflowExplore Google Cloud Storageand its use cases for data storage Get an introduction to Google Cloud SQL and its use cases for relational databases Get an introduction to Google Cloud Pub\/Sub and its use cases for real-time data streamingWho This Book Is ForData scientists, machine learning engineers, and analysts who want to learn how to use Google Cloud Platform (GCP) for their data science and big data projects\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003e\u003cp\u003eChapter 1: Introduction to GCP.- Chapter 2: Google Colaboratory.- Chapter 3: Big Data and Machine Learning.- Chapter 4: Data Visualization and Business Intelligence.- Chapter 5: Data Processing and Transformation.- Chapter 6: Data Analytics and Storage.- Chapter 7: Advanced Topics.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e","brand":"APress","offers":[{"title":"Default Title","offer_id":48739670163799,"sku":"9781484296875","price":38.24,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781484296875.jpg?v=1720052865"},{"product_id":"knowledge-risk-and-its-mitigation-practices-and-cases-9781789739206","title":"Knowledge Risk and its Mitigation: Practices and","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003eThe life cycle of companies and enterprises, at present, is short-lived due to rapid social and technological changes. Despite the growing awareness on the importance of knowledge management (KM) among academic researchers, it is still not widely practiced in industry. Why is this?\u003cbr\u003e  Most KM programs emphasize the importance of capturing, retaining, and sharing organisational knowledge amongst their stakeholders. The beneficial effect of these programs is rarely felt immediately, which often results in senior management avoiding prioritising KM initiatives. To overcome this hurdle in implementing KM an approach that includes the assessment of knowledge risk factors and the disastrous effect on the daily operation of the company is explored. \u003cbr\u003e  This book is the first attempt of its kind to provide a pragmatic view to launch knowledge risk management at the grassroot level, with steps by steps on what should be the mission and practical skills needed for a KM practitioner. Another surprise of this book is the numerous cases, examples and data that are brough about from the real business world. For business practitioners, KM researchers and those in HR, risk management, management accounting and Leadership this work is a must for expanding their understanding of Knowledge Management and knowledge risks.\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003eChapter 1. IntroductionChapter 2. Assessment of Knowledge Risks Chapter 3. Intellectual Capital Charting, Accounting and Risks Chapter 4. Knowledge Audit Chapter 5. Knowledge Elicitation  for Unstructured Business Process Chapter 6. Building  a Learning Organization Chapter 7. KM Implementation Chapter 8. Measuring  Corporate Performance","brand":"Emerald Publishing Limited","offers":[{"title":"Default Title","offer_id":48741686772055,"sku":"9781789739206","price":999.99,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781789739206.jpg?v=1720058430"},{"product_id":"knowledge-graphs-methodology-tools-and-selected-use-cases-9783030374389","title":"Knowledge Graphs: Methodology, Tools and Selected","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003eThis book describes methods and tools that empower information providers to build and maintain knowledge graphs, including those for manual, semi-automatic, and automatic construction; implementation; and validation and verification of semantic annotations and their integration into knowledge graphs. It also presents lifecycle-based approaches for semi-automatic and automatic curation of these graphs, such as approaches for assessment, error correction, and enrichment of knowledge graphs with other static and dynamic resources.\u003cbr\u003eChapter 1 defines knowledge graphs, focusing on the impact of various approaches rather than mathematical precision. Chapter 2 details how knowledge graphs are built, implemented, maintained, and deployed. Chapter 3 then introduces relevant application layers that can be built on top of such knowledge graphs, and explains how inference can be used to define views on such graphs, making it a useful resource for open and service-oriented dialog systems. Chapter 4 discusses applications of knowledge graph technologies for e-tourism and use cases for other verticals. Lastly, Chapter 5 provides a summary and sketches directions for future work. The additional appendix introduces an abstract syntax and semantics for domain specifications that are used to adapt schema.org to specific domains and tasks.\u003cbr\u003eTo illustrate the practical use of the approaches presented, the book discusses several pilots with a focus on conversational interfaces, describing how to exploit knowledge graphs for e-marketing and e-commerce. It is intended for advanced professionals and researchers requiring a brief introduction to knowledge graphs and their implementation. \u003cbr\u003e                 \u003cbr\u003e\u003cbr\u003e       \u003cbr\u003e          \u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003eIntroduction: What is a Knowledge Graph?.- How to build a Knowledge Graph.- How to use a Knowledge Graph.- Why we need Knowledge Graphs: Applications.- Conclusions.- References.- Appendix.- Index.","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743034421591,"sku":"9783030374389","price":47.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030374389.jpg?v=1720063823"},{"product_id":"intelligent-mobile-service-computing-9783030501839","title":"Intelligent Mobile Service Computing","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003eThis book discusses recent research and applications in intelligent service computing in mobile environments. The authors first explain how advances in artificial intelligence and big data have allowed for an array of intelligent services with complex and diverse applications. They then show how this brings new opportunities and challenges for service computing. The book, made up of contributions from academic and industry, aims to present advances in intelligent services, new algorithms and techniques in the field, foundational theory and systems, as well as practical real-life applications. Some of the topics discussed include cognition, modeling, description and verification for intelligent services; discovery, recommendation and selection for intelligent services; formal verification, testing and inspection for intelligent services; and composition and cooperation methods for intelligent services.\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003eChapter 1. Cross Domain security model based on Internet of vehicles .- Chapter 2. A framework to improve performance and energy efficiency of Embedded Intelligence Service System.- Chapter 3. Formal verification, testing, inspection for intelligent services.- Chapter 4. QoS for 5G Mobile Services based on Intelligent Multi-Access Edge Computing.- Chapter 5. An Efficient Interpolation Method through Trends Prediction in Smart Power Grid.- Chapter 6. 2PC+: A High Performance Protocol for Distributed Transactions of Microservice Architecture.","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743038091607,"sku":"9783030501839","price":999.99,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030501839.jpg?v=1791480492"},{"product_id":"designing-data-spaces-the-ecosystem-approach-to-competitive-advantage-9783030939748","title":"Designing Data Spaces: The Ecosystem Approach to","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003e\u003cp\u003eThis open access book provides a comprehensive view on data ecosystems and platform economics from methodical and technological foundations up to reports from practical implementations and applications in various industries.\u003c\/p\u003e  \u003cp\u003eTo this end, the book is structured in four parts: Part I “Foundations and Contexts” provides a general overview about building, running, and governing data spaces and an introduction to the IDS and GAIA-X projects. Part II “Data Space Technologies” subsequently details various implementation aspects of IDS and GAIA-X, including eg data usage control, the usage of blockchain technologies, or semantic data integration and interoperability. Next, Part III describes various “Use Cases and Data Ecosystems” from various application areas such as agriculture, healthcare, industry, energy, and mobility. Part IV eventually offers an overview of several “Solutions and Applications”, eg including products and experiences from companies like Google, SAP, Huawei, T-Systems, Innopay and many more.\u003c\/p\u003e  \u003cp\u003eOverall, the book provides professionals in industry with an encompassing overview of the technological and economic aspects of data spaces, based on the International Data Spaces and Gaia-X initiatives. It presents implementations and business cases and gives an outlook to future developments. In doing so, it aims at proliferating the vision of a social data market economy based on data spaces which embrace trust and data sovereignty.\u003cbr\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003ePart I: Foundations and Context.- 1. The Evolution of Data Spaces.- 2. How to Build, Run, and Govern Data Spaces.- 3. International Data Spaces in a Nutshell.- 4. Role of Gaia-X in the European Data Space Ecosystem.- 5. Legal Aspects of IDS: Data Sovereignty—What Does It Imply?.- 6. Tokenomics: Decentralized Incentivization in the Context of Data Spaces.- Part II: Data Space Technologies.- 7. The IDS Information Model: A Semantic Vocabulary for Sovereign Data Exchange.- 8. Data Usage Control.- 9. Building Trust in Data Spaces.- 10. Blockchain Technology and International Data Spaces.- 11. Federated Data Integration in Data Spaces.- 12. Semantic Integration and Interoperability.- 13. Data Ecosystems: A New Dimension of Value Creation Using AI and Machine Learning.- 14. IDS as a Foundation for Open Data Ecosystems.- 15. Defining Platform Research Infrastructure as a Service (PRIaaS) for Future Scientific Data Infrastructure.- Part III: Use Cases and Data Ecosystems.- 16. Silicon Economy: Logistics as the Natural Data Ecosystem.- 17. Agricultural Data Space.- 18. Medical Data Spaces in Healthcare Data Ecosystems.- 19. Industrial Data Spaces.- 20. Energy Data Space.- 21. Mobility Data Space.- Part IV: Solutions and Applications.- 22. Data Sharing Spaces: The BDVA Perspective.- 23. Data Platform Solutions.- 24. FIWARE for Data Spaces.- 25. Sovereign Cloud Technologies for Scalable Data Spaces.- 26. Data Space Based on Mass Customization Model.- 27. Huawei and International Data Spaces.- International Collaboration Between Data Spaces and Carrier\u003cp\u003e\u003c\/p\u003e  Networks.- 29. From Linear Supply Chains to Open Supply Ecosystems.- 30. Data Spaces: First Applications in Mobility and Industry.- 31. Competition, Security, and Transparency: Data in Connected Vehicles.- Data Space Functionality.- The Energy Data Space: The Path to a European Approach for Energy.","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743059980631,"sku":"9783030939748","price":999.99,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030939748.jpg?v=1791480568"},{"product_id":"machine-learning-for-text-9783030966225","title":"Machine Learning for Text","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003eThis second edition textbook covers a coherently organized framework for text analytics, which integrates material drawn from the intersecting topics of information retrieval, machine learning, and natural language processing. Particular importance is placed on deep learning methods. The chapters of this book span three broad categories:1. Basic algorithms: Chapters 1 through 7 discuss the classical algorithms for text analytics such as preprocessing, similarity computation, topic modeling, matrix factorization, clustering, classification, regression, and ensemble analysis.\u003cp\u003e\u003c\/p\u003e\u003cp\u003e2. Domain-sensitive learning and information retrieval: Chapters 8 and 9 discuss learning models in heterogeneous settings such as a combination of text with multimedia or Web links. The problem of information retrieval and Web search is also discussed in the context of its relationship with ranking and machine learning methods. \u003c\/p\u003e3. Natural language processing: Chapters 10 through 16 discuss various sequence-centric and natural language applications, such as feature engineering, neural language models, deep learning, transformers, pre-trained language models, text summarization, information extraction, knowledge graphs, question answering, opinion mining, text segmentation, and event detection. \u003cp\u003e\u003c\/p\u003e\u003cp\u003eCompared to the first edition, this second edition textbook (which targets mostly advanced level students majoring in computer science and math) has substantially more material on deep learning and natural language processing. Significant focus is placed on topics like transformers, pre-trained language models, knowledge graphs, and question answering.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003e1 An Introduction to Text Analytics.- 2 Text Preparation and Similarity Computation.- 3 Matrix Factorization and Topic Modeling.- 4 Text Clustering.- 5 Text Classification: Basic Models.- 6 Linear Models for Classification and Regression.- 7 Classifier Performance and Evaluation.- 8 Joint Text Mining with Heterogeneous Data.- 9 Information Retrieval and Search Engines.- 10 Language Modeling and Deep Learning.- 11 Attention Mechanisms and Transformers.- 12 Text Summarization.- 13 Information Extraction and Knowledge Graphs.- 14 Question Answering.- 15 Opinion Mining and Sentiment Analysis.- 16 Text Segmentation and Event Detection.","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743062110551,"sku":"9783030966225","price":51.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030966225.jpg?v=1720063944"},{"product_id":"model-based-safety-and-assessment-8th-international-symposium-imbsa-2022-munich-germany-september-5-7-2022-proceedings-9783031158414","title":"Model-Based Safety and Assessment: 8th","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003eThis book constitutes the proceedings of the 8th International Symposium on Model-Based Safety and Assessment, IMBSA 2022, held in Munich, Germany, in September 2022. The 15 revised full papers and 3 short papers presented were carefully reviewed and selected from 27 initial submissions. The papers focus on model-based and automated ways of assessing safety and other attributes of dependability of complex systems. They are organized in topical sections on safety analysis automation, MBSA practices, causal models and failure modeling strategies, designing mitigations of faults and attacks, data based safety analysis, dynamic risk assessment.\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003eSafety analysis automation.- An AEBS use case for Model-Based System Design integrating Safety Analyses and Simulation.-  COMPASTA: Extending TASTE with formal design and verification functionality.- MBSA practices.- MBSA in aeronautics: a way to support safety activities.- Modeling the Variability of System Safety Analysis using State-Machine Diagrams.- Model-Based Safety Analysis: a Practical Experience.- Practical application of Model-Based Safety Analysis to the Design of Global Operating System of new rolling stock on automatic metro lines.- Plug-and-Produce... safely! End-to-End Model-Based Safety Assurance for Reconfigurable Industry 4.0.- Causal models and failure modeling strategies.- Strategies for modelling failure propagation in dynamic systems with AltaRica.- Towards Causal Model-based Engineering in Automotive System Safety.- Performance assessment of an offshore windmill farm using AltaRica 3.0.- Component Fault and Deficiency Tree (CFDT): Combining functional safety and SOTIF Analysis.- Designing mitigations of Faults and Attacks.- A Capella-based Tool for the Early Assessment of Nano\/Micro Satellites Availability.- Data based safety analysis.- A Deep Learning Framework for Wind Turbine Repair Action Prediction Using Alarm Sequences and Long Short Term Memory Algorithms.- Tool Paper: Time Series Anomaly Detection Platform for MATLAB Simulink.- Keep your Distance: Determining Sampling and Distance Thresholds in Machine Learning Monitoring.- Dynamic risk assessment.- Engineering Dynamic Risk and Capability Models to Improve Cooperation Efficiency Between Human Workers and Autonomous Mobile Robots in Shared Spaces.- SafeDrones: Real-Time Reliability Evaluation of UAVs using Executable Digital Dependable Identities.","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":48743073743191,"sku":"9783031158414","price":42.74,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031158414.jpg?v=1791480632"},{"product_id":"data-mining-the-textbook-9783319381169","title":"Data Mining: The Textbook","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003e\u003cp\u003eThis textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues. It goes beyond the traditional focus on data mining problems to introduce advanced data types such as text, time series, discrete sequences, spatial data, graph data, and social networks. Until now, no single book has addressed all these topics in a comprehensive and integrated way. The chapters of this book fall into one of three categories: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eFundamental chapters: Data mining has four main problems, which correspond to clustering, classification, association pattern mining, and outlier analysis. These chapters comprehensively discuss a wide variety of methods for these problems. \u003c\/li\u003e\n\u003cli\u003eDomain chapters: These chapters discuss the specific methods used for different domains of data such as text data, time-series data, sequence data, graph data, and spatial data. \u003c\/li\u003e\n\u003cli\u003eApplication chapters: These chapters study important applications such as stream mining, Web mining, ranking, recommendations, social networks, and privacy preservation. The domain chapters also have an applied flavor. \u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eAppropriate for both introductory and advanced data mining courses, Data Mining: The Textbook balances mathematical details and intuition. It contains the necessary mathematical details for professors and researchers, but it is presented in a simple and intuitive style to improve accessibility for students and industrial practitioners (including those with a limited mathematical background). Numerous illustrations, examples, and exercises are included, with an emphasis on semantically interpretable examples.\u003c\/p\u003e\u003cp\u003ePraise for Data Mining: The Textbook - \u003c\/p\u003e\u003cp\u003e“As I read through this book, I have already decided to use it in my classes.  This is a book written by an outstanding researcher who has made fundamental contributions to data mining, in a way that is both accessible and up to date.  The book is complete with theory and practical use cases.  It’s a must-have for students and professors alike!\" -- Qiang Yang, Chair of Computer Science and Engineering at Hong Kong University of Science and Technology\u003c\/p\u003e\u003cp\u003e\"This is the most amazing and comprehensive text book on data mining. It covers not only the fundamental problems, such as clustering, classification, outliers and frequent patterns, and different data types, including text, time series, sequences, spatial data and graphs, but also various applications, such as recommenders, Web, social network and privacy.  It is a great book for graduate students and researchers as well as practitioners.\" -- Philip S. Yu, UIC Distinguished Professor and Wexler Chair in Information Technology at University of Illinois at Chicago\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTrade Review\u003c\/h2\u003e\u003cp\u003e“I can strongly recommend this book to any graduate students who want to learn the theoretical parts of the broad area of data mining. It offers enough material for several semesters of data mining or machine learning courses. Researchers and practitioners who want to survey the principles and concepts of current data mining topics and learn their theoretical perspective would benefit greatly from this book.” (Daijin Ko, Mathematical Reviews, May, 2017)\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e“Written by one of the most prodigious editors and authors in the data mining community, \u003ci\u003eData mining: the textbook \u003c\/i\u003eis a comprehensive introduction to the fundamentals and applications of data mining. The recent drive in industry and academic toward data science and more specifically “big data” makes any well-written book on this topic a welcome addition to the bookshelves of experienced and aspiring data scientists… The writing style is excellent and the author managed to provide sufficient mathematical background in terms of formal proofs and notations, in order to make it self-contained and scientifically appealing to more theory-oriented readers.Covering more than 20 chapters and 700 pages, Aggarwal provides a unique textbook and reference to data mining, which I recommend to every reader working on or learning about data mining.” (Radu State, ACM Computing Reviews #CR143869)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003eIntroduction to Data Mining.- Data Preparation.- Similarity and Distances.- Association Pattern Mining.- Association Pattern Mining: Advanced Concepts.- Cluster Analysis.- Cluster Analysis: Advanced Concepts.- Outlier Analysis.- Outlier Analysis: Advanced Concepts.- Data Classification.- Data Classification: Advanced Concepts.- Mining Data Streams.- Mining Text Data.- Mining Time-Series Data.- Mining Discrete Sequences.- Mining Spatial Data.- Mining Graph Data.- Mining Web Data.- Social Network Analysis.- Privacy-Preserving Data Mining.","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":48743094944087,"sku":"9783319381169","price":999.99,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783319381169.jpg?v=1720064090"},{"product_id":"introduction-to-data-science-a-python-approach-to-concepts-techniques-and-applications-9783319500164","title":"Introduction to Data Science: A Python Approach","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003e\u003cp\u003eThis accessible and classroom-tested textbook\/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, and the practical application of data science for such tasks as building recommender systems or performing sentiment analysis. Topics and features: provides numerous practical case studies using real-world data throughout the book; supports understanding through hands-on experience of solving data science problems using Python; describes techniques and tools for statistical analysis, machine learning, graph analysis, and parallel programming; reviews a range of applications of data science, including recommender systems and sentiment analysis of text data; provides supplementary code resources and data at an associated website.\u003cbr\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTrade Review\u003c\/h2\u003e\u003cp\u003e“This book contains a broad range of timely topics and presents interesting examples on real-life data using Python. … the book is a good addition to references on Python and data science. Students as well as practicing data scientists and engineers will benefit from the many techniques and use cases presented in the book.” (Computing Reviews, December, 2017)\u003c\/p\u003e“The book ‘Introduction to Data Science’ is built as a starter presentation of concepts, techniques and approaches that constitute the initial contact with data science for scientists … . The style of the book recommends it to both undergraduates and postgraduates and the concluding remarks and references provide guidance for the next steps in the study of particular topics.” (Irina Ioana Mohorianu, zbMATH, Vol. 1365.62003, 2017)\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003e\u003cp\u003eIntroduction to Data Science\u003cbr\u003e \u003ci\u003eJordi Vitrià\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003eToolboxes for Data Scientists\u003cbr\u003e \u003ci\u003eEloi Puertas and Francesc Dantí\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003eDescriptive statistics\u003cbr\u003e \u003ci\u003ePetia Radeva and Laura Igual\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003eStatistical Inference\u003cbr\u003e \u003ci\u003eJordi Vitrià and Sergio Escalera\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003eSupervised Learning\u003cbr\u003e \u003ci\u003eOriol Pujol and Petia Radeva\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003eRegression Analysis\u003cbr\u003e \u003ci\u003eLaura Igual and Jordi Vitrià\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003eUnsupervised Learning\u003cbr\u003e \u003ci\u003ePetia Radeva and Oriol Pujol\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003eNetwork Analysis\u003cbr\u003e \u003ci\u003eLaura Igual and Santi Seguí\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003eRecommender Systems\u003cbr\u003e \u003ci\u003eSanti Seguí and Eloi Puertas\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003eStatistical Natural Language Processing for Sentiment Analysis\u003cbr\u003e \u003ci\u003eSergio Escalera and Santi Seguí\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003eParallel Computing\u003cbr\u003e \u003ci\u003eFrancesc Dantí and Lluís Garrido\u003c\/i\u003e\u003c\/p\u003e","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":48743096549719,"sku":"9783319500164","price":34.19,"currency_code":"GBP","in_stock":false}]},{"product_id":"the-data-science-design-manual-9783319554433","title":"The Data Science Design Manual","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003e\u003cp\u003eThis engaging and clearly written textbook\/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data.\u003c\/p\u003e  \u003cp\u003e\u003cb\u003e\u003ci\u003eThe Data Science Design Manual\u003c\/i\u003e\u003c\/b\u003e is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles.\u003c\/p\u003e  This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well.\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eAdditional learning tools:\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eContains “War Stories,” offering perspectives on how data science applies in the real world\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eIncludes “Homework Problems,” providing a wide range of exercises and projects for self-study\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eProvides a complete set of lecture slides and online video lectures at www.data-manual.com\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eProvides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapter\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eRecommends exciting “Kaggle Challenges” from the online platform Kaggle\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eHighlights “False Starts,” revealing the subtle reasons why certain approaches fail\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eOffers examples taken from the data science television show “The Quant Shop” (www.quant-shop.com)\u003cbr\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e\u003c\/p\u003e  \u003cp\u003e\u003c\/p\u003e  \u003cp\u003e\u003c\/p\u003e  \u003cp\u003e\u003c\/p\u003e  \u003cp\u003e\u003c\/p\u003e  \u003cp\u003e\u003c\/p\u003e  \u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTrade Review\u003c\/h2\u003e\u003cp\u003e \u003c\/p\u003e\u003cp\u003e“The book is more than a typical manual. In fact, the author himself designates it as a textbook for an introductory course on data science. The chapters are richly equipped with exercises. The topics are always explained starting with a proper motivation and continuing with practical examples. This is perhaps the most outstanding feature of the book. It can serve as a regular textbook for an academic course. In fact, I should like to recommend it exactly for this purpose. On the other hand, it provides a wealth of material for people from industry, such as software engineers, and can serve as a manual for them to accomplish data science tasks. It should be noted that the book is not just a text, but a much more complex product, including a full set of lecture slides available online as well as a solutions wiki.” (P. Navrat, Computing Reviews, February, 23, 2018)\u003c\/p\u003e  \u003cp\u003e\u003c\/p\u003e  ​\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003eWhat is Data Science?\u003cp\u003e\u003c\/p\u003e  \u003cp\u003eMathematical Preliminaries\u003c\/p\u003e  \u003cp\u003eData Munging\u003c\/p\u003e  \u003cp\u003eScores and Rankings\u003c\/p\u003e  \u003cp\u003eStatistical Analysis\u003c\/p\u003e  \u003cp\u003eVisualizing Data\u003c\/p\u003e  \u003cp\u003eMathematical Models\u003c\/p\u003e  \u003cp\u003eLinear Algebra\u003c\/p\u003e  \u003cp\u003eLinear and Logistic Regression\u003c\/p\u003e  \u003cp\u003eDistance and Network Methods\u003c\/p\u003e  \u003cp\u003eMachine Learning\u003c\/p\u003e  \u003cp\u003eBig Data: Achieving Scale\u003c\/p\u003e","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":48743098220887,"sku":"9783319554433","price":45.55,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783319554433.jpg?v=1791379135"},{"product_id":"algorithmic-intelligence-towards-an-algorithmic-foundation-for-artificial-intelligence-9783319655956","title":"Algorithmic Intelligence: Towards an Algorithmic","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003e\u003cp\u003eIn this book the author argues that the basis of what we consider computer intelligence has algorithmic roots, and he presents this with a holistic view, showing examples and explaining approaches that encompass theoretical computer science and machine learning via engineered algorithmic solutions.\u003c\/p\u003e\u003cp\u003ePart I of the book introduces the basics. The author starts with a hands-on programming primer for solving combinatorial problems, with an emphasis on recursive solutions. The other chapters in the first part of the book explain shortest paths, sorting, deep learning, and Monte Carlo search. \u003c\/p\u003e\u003cp\u003eA key function of computational tools is processing Big Data efficiently, and the chapters in Part II of the book examine traditional graph problems such as finding cliques, colorings, independent sets, vertex covers, and hitting sets, and the subsequent chapters cover multimedia, network, image, and navigation data. \u003c\/p\u003e\u003cp\u003eThe highly topical research areas detailed in Part III are machine learning, problem solving, action planning, general game playing, multiagent systems, and recommendation and configuration. \u003c\/p\u003e\u003cp\u003eFinally, in Part IV the author uses application areas such as model checking, computational biology, logistics, additive manufacturing, robot motion planning, and industrial production to explain how the techniques described may be exploited in modern settings.\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eThe book is supported with a comprehensive index and references, and it will be of value to researchers, practitioners, and students in the areas of artificial intelligence and computational intelligence.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003e\u003cp\u003ePreface.- Towards a Characterization.- Part I, Basics.- 1. Programming Primer.- 2. Shortest Paths.- 3. Sorting.- 4. Deep Learning.- 5. Monte-Carlo Search.- Part II, Big Data.- 6. Graph data.- 7. Multimedia Data.- 8. Network Data.- 9. Image Data.- 10. Navigation Data.- Part III, Research Areas.- 11. Machine Learning.- 12. Problem Solving.- 13. Card Game Playing.- 14. Action Planning.- 15. General Game Playing.- 16. Multiagent Systems.- 17. Recommendation and Configuration Part IV, Applications.- 18. Adversarial Planning.- 19. Model Checking.- 20. Computational Biology.- 21. Logistics.- 22. Additive Manufacturing.- 23. Robot Motion Planning.- 24. Industrial Production.- 25. Further Application Areas. - Index and References\u003c\/p\u003e","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":48743101235543,"sku":"9783319655956","price":170.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783319655956.jpg?v=1720064115"},{"product_id":"data-matching-concepts-and-techniques-for-record-linkage-entity-resolution-and-duplicate-detection-9783642430015","title":"Data Matching: Concepts and Techniques for Record","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003e\u003cp\u003eData matching (also known as record or data linkage, entity resolution, object identification, or field matching) is the task of identifying, matching and merging records that correspond to the same entities from several databases or even within one database. Based on research in various domains including applied statistics, health informatics, data mining, machine learning, artificial intelligence, database management, and digital libraries, significant advances have been achieved over the last decade in all aspects of the data matching process, especially on how to improve the accuracy of data matching, and its scalability to large databases.\u003c\/p\u003e\u003cp\u003ePeter Christen’s book is divided into three parts: Part I, “Overview”, introduces the subject by presenting several sample applications and their special challenges, as well as a general overview of a generic data matching process. Part II, “Steps of the Data Matching Process”, then details its main steps like pre-processing, indexing, field and record comparison, classification, and quality evaluation. Lastly, part III, “Further Topics”, deals with specific aspects like privacy, real-time matching, or matching unstructured data. Finally, it briefly describes the main features of many research and open source systems available today.\u003c\/p\u003eBy providing the reader with a broad range of data matching concepts and techniques and touching on all aspects of the data matching process, this book helps researchers as well as students specializing in data quality or data matching aspects to familiarize themselves with recent research advances and to identify open research challenges in the area of data matching. To this end, each chapter of the book includes a final section that provides pointers to further background and research material. Practitioners will better understand the current state of the art in data matching as well as the internal workings and limitations of current systems. Especially, they will learn that it is often not feasible to simply implement an existing off-the-shelf data matching system without substantial adaption and customization. Such practical considerations are discussed for each of the major steps in the data matching process.\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTrade Review\u003c\/h2\u003e\u003ci\u003e\"The book is very well organized and exceptionally well written. Because of the depth, amount, and quality of the material that is covered, I would expect this book to be one of the standard references in future years.\"\u003c\/i\u003e William E. Winkler, U.S. Bureau of the Census, Washington, DC, USA\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003e\u003cp\u003ePart I Overview.- Introduction.- The Data Matching Process.- Part II Steps of the Data Matching Process.- Data Pre-Processing.- Indexing.- Field and Record Comparison.- Classification.- Evaluation of Matching Quality and Complexity.- Part III Further Topics.- Privacy Aspects of Data Matching.- Further Topics and Research Directions.- Data Matching Systems.\u003c\/p\u003e","brand":"Springer-Verlag Berlin and Heidelberg GmbH \u0026 Co. KG","offers":[{"title":"Default Title","offer_id":48743135838551,"sku":"9783642430015","price":999.99,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783642430015.jpg?v=1791482056"},{"product_id":"real-time-c-efficient-object-oriented-and-template-microcontroller-programming-9783662629987","title":"Real-Time C++: Efficient Object-Oriented and","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003e\u003cp\u003eWith this book, Christopher Kormanyos delivers a highly practical guide to programming real-time embedded microcontroller systems in C++. It is divided into three parts plus several appendices. Part I provides a foundation for real-time C++ by covering language technologies, including object-oriented methods, template programming and optimization. Next, part II presents detailed descriptions of a variety of C++ components that are widely used in microcontroller programming. It details some of C++’s most powerful language elements, such as class types, templates and the STL, to develop components for microcontroller register access, low-level drivers, custom memory management, embedded containers, multitasking, etc. Finally, part III describes mathematical methods and generic utilities that can be employed to solve recurring problems in real-time C++. The appendices include a brief C++ language tutorial, information on the real-time C++ development environment and instructions for building GNU GCC cross-compilers and a microcontroller circuit.\u003c\/p\u003e  \u003cp\u003eFor this fourth edition, the most recent specification of C++20 is used throughout the text. Several sections on new C++20 functionality have been added, and various others reworked to reflect changes in the standard. Also several new example projects ranging from introductory to advanced level are included and existing ones extended, and various reader suggestions have been incorporated. Efficiency is always in focus and numerous examples are backed up with runtime measurements and size analyses that quantify the true costs of the code down to the very last byte and microsecond.\u003c\/p\u003e  \u003cp\u003eThe target audience of this book mainly consists of students and professionals interested in real-time C++. Readers should be familiar with C or another programming language and will benefit most if they have had some previous experience with microcontroller electronics and the performance and size issues prevalent in embedded systems programming.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cbr\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003ePart I: Language Technologies for Real-Time C++.- 1. Getting Started with Real-Time C++.- 2. Working with a Real-Time C++ Program on a Board.- 3. An Easy Jump Start in Real-Time C++.- 4. Object-Oriented Techniques for Microcontrollers.- 5. C++ Templates for Microcontrollers.- 6. Optimized C++ Programming for Microcontrollers.- Part II: Components for Real-Time C++.- 7. Accessing Microcontroller Registers.- 8. The Right Start.- 9. Low-Level Hardware Drivers in C++.- 10. Custom Memory Management.- 11. C++ Multitasking.- Part III: Mathematics and Utilities for Real-Time C++.- 12. Floating-Point Mathematics.-       13. Fixed-Point Mathematics.- 14. High-Performance Digital Filters.- 15. C++ Utilities.- 16. Extending the C++ Standard Library and the STL.- 17. Using C-Language Code in C++.- Additional Reading.","brand":"Springer-Verlag Berlin and Heidelberg GmbH \u0026 Co. KG","offers":[{"title":"Default Title","offer_id":48743142916439,"sku":"9783662629987","price":40.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783662629987.jpg?v=1720064297"},{"product_id":"the-elements-of-statistical-learning-springer-series-in-statistics-9780387848570","title":"The Elements of Statistical Learning Springer","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003eOverview of Supervised Learning.- Linear Methods for Regression.- Linear Methods for Classification.- Basis Expansions and Regularization.- Kernel Smoothing Methods.- Model Assessment and Selection.- Model Inference and Averaging.- Additive Models, Trees, and Related Methods.- Boosting and Additive Trees.- Neural Networks.- Support Vector Machines and Flexible Discriminants.- Prototype Methods and Nearest-Neighbors.- Unsupervised Learning.- Random Forests.- Ensemble Learning.- Undirected Graphical Models.- High-Dimensional Problems: p ? N.\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTrade Review\u003c\/h2\u003e\u003cp\u003eFrom the reviews:\u003c\/p\u003e\u003cp\u003e\"Like the first edition, the current one is a welcome edition to researchers and academicians equally…. Almost all of the chapters are revised.… The Material is nicely reorganized and repackaged, with the general layout being the same as that of the first edition.… If you bought the first edition, I suggest that you buy the second editon for maximum effect, and if you haven’t, then I still strongly recommend you have this book at your desk. Is it a good investment, statistically speaking!\" (Book Review Editor, \u003ci\u003eTechnometrics\u003c\/i\u003e, August 2009, VOL. 51, NO. 3)\u003c\/p\u003e\u003cp\u003eFrom the reviews of the second edition:\u003c\/p\u003e\u003cp\u003e\"This second edition pays tribute to the many developments in recent years in this field, and new material was added to several existing chapters as well as four new chapters … were included. … These additions make this book worthwhile to obtain … . In general this is a well written book which gives a good overview on statistical learning and can be recommended to everyone interested in this field. The book is so comprehensive that it offers material for several courses.\" (Klaus Nordhausen, International Statistical Review, Vol. 77 (3), 2009)\u003c\/p\u003e\u003cp\u003e“The second edition … features about 200 pages of substantial new additions in the form of four new chapters, as well as various complements to existing chapters. … the book may also be of interest to a theoretically inclined reader looking for an entry point to the area and wanting to get an initial understanding of which mathematical issues are relevant in relation to practice. … this is a welcome update to an already fine book, which will surely reinforce its status as a reference.” (Gilles Blanchard, Mathematical Reviews, Issue 2012 d)\u003c\/p\u003e\u003cp\u003e“The book would be ideal for statistics graduate students … . This book really is the standard in the field, referenced in most papers and books on the subject, and it is easy to see why. The book is very well written, with informative graphics on almost every other page. It looks great and inviting. You can flip the book open to any page, read a sentence or two and be hooked for the next hour or so.” (Peter Rabinovitch, The Mathematical Association of America, May, 2012)\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003eIntroduction.- Overview of supervised learning.- Linear methods for regression.- Linear methods for classification.- Basis expansions and regularization.- Kernel smoothing methods.- Model assessment and selection.- Model inference and averaging.- Additive models, trees, and related methods.- Boosting and additive trees.- Neural networks.- Support vector machines and flexible discriminants.- Prototype methods and nearest-neighbors.- Unsupervised learning.","brand":"Springer-Verlag New York Inc.","offers":[{"title":"Default Title","offer_id":48864540197207,"sku":"9780387848570","price":61.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780387848570.jpg?v=1722272386"},{"product_id":"the-master-algorithm-9780465094271","title":"The Master Algorithm","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003e\u003cdiv\u003e\u003cdiv\u003e\n\u003cb\u003eRecommended by Bill Gates\u003cbr\u003e\u003cbr\u003e A thought-provoking and wide-ranging exploration of machine learning and the race to build computer intelligences as flexible as our own\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e In the world's top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In \u003ci\u003eThe Master Algorithm\u003c\/i\u003e, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner--the Master Algorithm--and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible. \u003c\/div\u003e\u003c\/div\u003e","brand":"INGRAM PUBLISHER SERVICES US","offers":[{"title":"Default Title","offer_id":48864620282199,"sku":"9780465094271","price":14.4,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780465094271.jpg?v=1722272760"},{"product_id":"principles-of-database-management-9781107186125","title":"Principles of Database Management","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003eThis comprehensive textbook teaches the fundamentals of database design, modeling, systems, data storage, and the evolving world of data warehousing, governance and more. Written by experienced educators and experts in big data, analytics, data quality, and data integration, it provides an up-to-date approach to database management. This full-color, illustrated text has a balanced theory-practice focus, covering essential topics, from established database technologies to recent trends, like Big Data, NoSQL, and more. Fundamental concepts are supported by real-world examples, query and code walkthroughs, and figures, making it perfect for introductory courses for advanced undergraduates and graduate students in information systems or computer science. These examples are further supported by an online playground with multiple learning environments, including MySQL, MongoDB, Neo4j Cypher, and tree structure visualization. This combined learning approach connects key concepts throughout the text to the important, practical tools to get started in database management.\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTrade Review\u003c\/h2\u003e'Although there have been a series of classical textbooks on database systems, the new dramatic advances call for an updated text covering the latest significant topics, such as big data analytics, No-SQL and much more. Fortunately, this is exactly what this book has to offer. It is highly desirable for training the next generation of data management professionals.' Jian Pei, Simon Fraser University, Canada\u003cbr\u003e'I haven't seen an as up-to-date and comprehensive textbook for Database Management as this one in many years. Principles of Database Management combines a number of classical and recent topics concerning Data Modeling, Relational Databases, Object-Oriented Databases, XML, Distributed Data Management, NoSQL and Big Data in an unprecedented manner. The authors did a great job in stitching these topics into one coherent and compelling story that will serve as an ideal basis for teaching both introductory and advanced courses.' Martin Theobald, University of Luxembourg\u003cbr\u003e'This is a very timely book with outstanding coverage of database topics and excellent treatment of database details. It not only gives very solid discussions of traditional topics like data modeling and relational databases but also contains refreshing contents on frontier topics such as XML databases, NoSQL databases, big data, and analytics. For those reasons, this will be a good book for database professionals who will keep using it for all stages of database studies and works.' J. Leon Zhao, City University of Hong Kong\u003cbr\u003e'This accessible, authoritative book introduces the reader the most important fundamental concepts of data management, while providing a practical view of recent advances. Both are essential for data professionals today.' Foster Provost, New York University, Stern School of Business\u003cbr\u003e'This guide to big and small data management addresses both fundamental principles and practical deployment. It reviews a range of databases and their relevance for analytics. The book is useful to practitioners because it contains many case studies, links to open-source software, and a very useful abstraction of analytics that will help them better choose solutions. It is important to academics because it promotes database principles which are key to successful and sustainable data science.' Sihem Amer-Yahia, Laboratoire d'Informatique de Grenoble and Editor-in-Chief the International Journal on Very Large DataBases\u003cbr\u003e'This book covers everything you will need to teach in a database implementation and design class. With some chapters covering big data, analytic models\/methods, and No-SQL, it can keep our students up-to-date with these new technologies in data management related topics.' Han-fen Hu, University of Nevada, Las Vegas\u003cbr\u003e'As we are entering a new technological era of intelligent machines powered by data-driven algorithms, understanding fundamental concepts of data management and their most current practical applications has become more important than ever. This book is a timely guide for anyone interested in getting up to speed with the state of the art in database systems, big data technologies, and data science. It is full of insightful examples and case studies with direct industrial relevance.' Nesime Tatbul, Intel Labs and Massachusetts Institute of Technology\u003cbr\u003e'It is a pleasure to study this new book on database systems. The book offers a fantastically fresh approach to database teaching. The mix of theoretical and practical contents is almost perfect, the content is up-to-date and covers the recent ones, the examples are nice, and the database testbed provides an excellent way of understanding the concepts. Coupled with the authors 'expertise, this book is an important addition to the database field.' Arnab Bhattacharya, Indian Institute of Technology, Kanpur\u003cbr\u003e'Principles of Database Management is my favorite textbook for teaching a course on database management. Written in a well-illustrated style, this comprehensive book covers essential topics in established data management technologies and recent discoveries in data science. With a nice balance between theory and practice, it is not only an excellent teaching medium for students taking information management and\/or data analytics courses, but also a quick and valuable reference for scientists and engineers working in this area.' Chuan Xiao, Graduate School of Informatics, Nagoya University\u003cbr\u003e'Data science success stories and big data applications are only possible because of advances in database technology. This book provides both a broad and deep introduction to databases. It covers the different types of database systems (from relational to noSQL) and manages to bridge the gap between data modeling and the underlying basic principles. The book is highly recommended for anyone that wants to understand how modern information systems deal with ever-growing volumes of data.' Wil van der Aalst, RWTH Aachen University\u003cbr\u003e'The database field has been evolving for several decades and the need for updated textbooks is continuous. Now, this need is covered by this fresh book by Lemahieu, van den Broucke and Baesens. It spans from traditional topics - such as the relational model and SQL - to more recent topics – such as distributed computing with Hadoop and Spark as well as data analytics. The book can be used as an introductory text and for graduate courses.' Yannis Manolopoulos, Data Science \u0026amp; Engineering Lab, Aristotle University of Thessaloniki\u003cbr\u003e'I like the way the book covers both traditional database topics and newer material such as big data, No-SQL databases, and data quality. The coverage is just right for my course and the level of the material is very appropriate for my students. The book also has clear explanations and good examples.' Barbara Klein, University of Michigan\u003cbr\u003eThis book provides a unique perspective on database management and how to store, manage, and analyze small and big data. The accompanying exercises and solutions, cases, slides, and YouTube lectures turn it into an indispensable resource for anyone teaching an undergraduate or postgraduate course on the topic.' Wolfgang Ketter, Erasmus University Rotterdam\u003cbr\u003e'This is a very modern textbook that fills the needs of current trends without sacrificing the need to cover the required database management systems fundamentals.' George Dimitoglou, Hood College, Maryland\u003cbr\u003e'This book is a much needed foundational piece on data management and data science. The authors successfully integrate the fields of database technology, operations research and big data analytics, which have often been covered independently in the past. A key asset is its didactical approach that builds on a rich set of industry examples and exercises. The book is a must-read for all scholars and practitioners interested in database management, big data analytics and its applications.' Jan Mendling, Institute for Information Business, Vienna\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003ePreface; Part I. Databases and Database Design: 1. Fundamental concepts of database management; 2. Architecture and categorization of DBMSs; 3. Conceptual data modeling using the (E)ER model and UML class diagram; 4. Organizational aspects of data management; Part II. Types of Database Systems: 5. Legacy databases; 6. Relational databases: the relational model; 7. Relational databases: structured query language (SQL); 8. Object oriented databases and object persistence; 9. Extended relational databases; 10. XML databases; 11. NoSQL databases; Part III. Physical Data Storage, Transaction Management, and Database Access: 12. Physical file organization and indexing; 13. Physical database organization; 14. Basics of transaction management; 15. Accessing databases and database APIs; 16. Data distribution and distributed transaction management; Part IV. Data Warehousing, Data Governance and (Big) Data Analytics: 17. Data warehousing and business intelligence; 18. Data integration, data quality and data governance; 19. Big data; 20. Analytics; Appendix A. Cases and questions; Appendix B. Using the online environment; Appendix C. Answer key to select review questions; Glossary; Index.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48866339553623,"sku":"9781107186125","price":56.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781107186125.jpg?v=1722278206"},{"product_id":"genetic-algorithms-and-machine-learning-for-programmers-9781680506204","title":"Genetic Algorithms and Machine Learning for","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eSelf-driving cars, natural language recognition, and online recommendation engines are all possible thanks to Machine Learning. Now you can create your own genetic algorithms, nature-inspired swarms, Monte Carlo simulations, cellular automata, and clusters. Learn how to test your ML code and dive into even more advanced topics. If you are a beginner-to-intermediate programmer keen to understand machine learning, this book is for you.\u003c\/p\u003e \u003cp\u003eDiscover machine learning algorithms using a handful of self-contained recipes. Build a repertoire of algorithms, discovering terms and approaches that apply generally. Bake intelligence into your algorithms, guiding them to discover good solutions to problems.\u003c\/p\u003e \u003cp\u003eIn this book, you will: \u003c\/p\u003e \u003cul\u003e\n\u003cli\u003eUse heuristics and design fitness functions.\u003c\/li\u003e\n\u003cli\u003eBuild genetic algorithms.\u003c\/li\u003e\n\u003cli\u003eMake nature-inspired swarms with ants, bees and particles.\u003c\/li\u003e\n\u003cli\u003eCreate Monte Carlo simulations.\u003c\/li\u003e\n\u003cli\u003eInvestigate cellular automata.\u003c\/li\u003e\n\u003cli\u003eFind minima and maxima, using hill climbing and simulated annealing.\u003c\/li\u003e\n\u003cli\u003eTry selection methods, including tournament and roulette wheels.\u003c\/li\u003e\n\u003cli\u003eLearn about heuristics, fitness functions, metrics, and clusters.\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003eTest your code and get inspired to try new problems. Work through scenarios to code your way out of a paper bag; an important skill for any competent programmer. See how the algorithms explore and learn by creating visualizations of each problem. Get inspired to design your own machine learning projects and become familiar with the jargon.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eWhat You Need: \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCode in C++ (\u0026gt;= C++11), Python (2.x or 3.x) and JavaScript (using the HTML5 canvas). Also uses matplotlib and some open source libraries, including SFML, Catch and Cosmic-Ray. These plotting and testing libraries are not required but their use will give you a fuller experience. Armed with just a text editor and compiler\/interpreter for your language of choice you can still code along from the general algorithm descriptions.\u003c\/p\u003e","brand":"The Pragmatic Programmers","offers":[{"title":"Default Title","offer_id":48868033462615,"sku":"9781680506204","price":35.14,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781680506204.jpg?v=1722286107"},{"product_id":"codeless-data-structures-and-algorithms-9781484257241","title":"Codeless Data Structures and Algorithms","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePart 1: Data Structures.-\u003c\/p\u003e  \u003cp\u003eChapter 1: Intro to DSA, Types and Big-O.-\u003c\/p\u003e  \u003cp\u003eChapter 2: Linear Data Structures.-\u003c\/p\u003e  \u003cp\u003eChapter 3: Tree Data Structures.-\u003c\/p\u003e  \u003cp\u003eChapter 4: Hash Data Structures.-\u003c\/p\u003e  \u003cp\u003eChapter 5: Graphs.-\u003c\/p\u003e  \u003cp\u003ePart 2: Algorithms.-\u003c\/p\u003e  \u003cp\u003eChapter 6: Linear and Binary Search.-\u003c\/p\u003e  \u003cp\u003eChapter 7: Sorting Algorithms.-\u003c\/p\u003e  \u003cp\u003eChapter 8: Searching Algorithms.-\u003c\/p\u003e  \u003cp\u003eChapter 9: Clustering Algorithms.-\u003c\/p\u003e  \u003cp\u003eChapter 10: Randomness.-\u003c\/p\u003e  \u003cp\u003eChapter 11: Scheduling Algorithms.-\u003c\/p\u003e  \u003cp\u003eChapter 12: Algorithm Planning and Design.-\u003c\/p\u003e  \u003cp\u003eAppendix A: Going Further.-\u003c\/p\u003e","brand":"APress","offers":[{"title":"Default Title","offer_id":48885825012055,"sku":"9781484257241","price":29.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781484257241.jpg?v=1722537831"},{"product_id":"leveling-up-with-sql-9781484296844","title":"Leveling Up with SQL","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003eIntermediate-Advanced user level\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003e\u003cp\u003eChapter 1:  Getting Ready.- Chapter 2:  Working with Table Design.- Chapter 3:  Table Relationships and Working With Joins.- Chapter 4:  Working with Calculated Data.- Chapter 5:  Aggregating Data.- Chapter 6:  Creating and Using Views and Friends.- Chapter 7:  Working With Subqueries and Common Table Expressions.- Chapter 8:  Working With Window Functions.-Chapter 9: More on Common Table Expressions.- Chapter 10: More Techniques with SQL: Triggers, Pivot Tables, and Variables.- Appendix A.\u003c\/p\u003e","brand":"APress","offers":[{"title":"Default Title","offer_id":48885834154327,"sku":"9781484296844","price":33.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781484296844.jpg?v=1722537862"},{"product_id":"expert-systems-research-trends-9781600216886","title":"Expert Systems Research Trends","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003eAn expert system, also known as a knowledge based system, is a computer program that contains some of the subject-specific knowledge of one or more human experts. This class of program was first developed by researchers in artificial intelligence during the 1960s and 1970s and applied commercially throughout the 1980s. The most common form of expert systems is a program made up of a set of rules that analyse information usually supplied by the user of the system) about a specific class of problems, as well as providing mathematical analysis of the problem(s), and, depending upon their design, recommend a course of user action in order to implement corrections. It is a system that utilises what appear to be reasoning capabilities to reach conclusions. 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The most common form of expert systems is a program made up of a set of rules that analyse information (usually supplied by the user of the system) about a specific class of problems, as well as providing mathematical analysis of the problem(s), and, depending upon their design, recommend a course of user action in order to implement corrections. It is a system that utilises what appear to be reasoning capabilities to reach conclusions. 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Which side is likelier to get it right? Which will write simple and elegant code? Which one makes the smartest use of lesser-known Regex library features? Read the book to find out.\u003c\/p\u003e \u003cp\u003eDavid also offers AI best practices, showing how smart prompts return better results. By the end, you'll be a master at solving your own Regex puzzles, whether you use AI or not.\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eAbout the technology\u003c\/strong\u003e\u003c\/p\u003e \u003cp\u003eGround-breaking large language model research from OpenAI, Google, Amazon, and others, have transformed expectations of machine-generated software. But how do these AI assistants, like ChatGPT and GitHub Copilot, measure up against regular expressions—a workhorse technology for developers used to describe, find, and manipulate patterns in the text? Regular expressions are compact, complex, and subtle. 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Strongly recommended for anyone who is looking to use Artificial Intelligence for their business, to tap into its strength and prepare for potential risks.\"\u003cbr\u003e \u003cstrong\u003eKoo Ping Shung,\u003c\/strong\u003e Data Science Rex\u003c\/p\u003e","brand":"Manning Publications","offers":[{"title":"Default Title","offer_id":48887149986135,"sku":"9781633437814","price":999.99,"currency_code":"GBP","in_stock":false}]},{"product_id":"new-developments-in-expert-systems-research-9781634829069","title":"New Developments in Expert Systems Research","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e","brand":"Nova Science Publishers Inc","offers":[{"title":"Default Title","offer_id":48887196123479,"sku":"9781634829069","price":999.99,"currency_code":"GBP","in_stock":false}]},{"product_id":"knowledge-based-expert-systems-in-chemistry-artificial-intelligence-in-decision-making-9781788014717","title":"Knowledge-based Expert Systems in Chemistry:","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003eThere have been significant developments in the use of knowledge-based expert systems in chemistry since the first edition of this book was published in 2009. This new edition has been thoroughly revised and updated to reflect the advances.   The underlying theme of the book is still the need for computer systems that work with uncertain or qualitative data to support decision-making based on reasoned judgements. With the continuing evolution of regulations for the assessment of chemical hazards, and changes in thinking about how scientific decisions should be made, that need is ever greater. Knowledge-based expert systems are well established in chemistry, especially in relation to toxicology, and they are used routinely to support regulatory submissions. The effectiveness and continued acceptance of computer prediction depends on our ability to assess the trustworthiness of predictions and the validity of the models on which they are based.   Written by a pioneer in the field, this book provides an essential reference for anyone interested in the uses of artificial intelligence for decision making in chemistry.\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003eArtificial Intelligence – Making Use of Reasoning; Synthesis Planning by Computer;Other Programs to Support Chemical Synthesis Planning; International Repercussions of the Harvard LHASA Project; Current Interest in Synthesis Planning by Computer; Structure Representation; Structure, Substructure and Superstructure Searching; Protons That Come and Go; Aromaticity and Stereochemistry; DEREK – Predicting Toxicity; Other Alert-based Toxicity Prediction Systems; Rule Discovery; The 2D–3D Debate; Making Use of Reasoning: Derek for Windows; Predicting Metabolism; Relative Reasoning; Predicting Biodegradation; Other Applications and Potential Applications of Knowledge-based Prediction in Chemistry; Combining Predictions; The Adverse Outcome Pathways Approach; Evaluation of Knowledge-based Systems; Validation of Computer Predictions; Artificial Intelligence Developments in Other Fields; A Subjective View of the Future","brand":"Royal Society of Chemistry","offers":[{"title":"Default Title","offer_id":48887842603351,"sku":"9781788014717","price":141.55,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781788014717.jpg?v=1791477507"},{"product_id":"artificial-intelligence-for-improved-patient-outcomes-principles-for-moving-forward-with-rigorous-science-9781975197933","title":"Artificial Intelligence for Improved Patient","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003e\u003cp\u003e\u003cb\u003e\u003ci\u003eArtificial Intelligence for Improved Patient Outcomes\u003c\/i\u003e\u003c\/b\u003e provides new, relevant, and practical information on what AI can do in healthcare and how to assess whether AI is improving health outcomes.  \u003cbr\u003e   \u003c\/p\u003e\u003cp\u003e With clear insights and a balanced approach, this innovative book offers a one-stop guide on how to design and lead pragmatic real-world AI studies that yield rigorous scientific evidence—all in a manner that is safe and ethical. 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Attack,\u003c\/div\u003e\u003cdiv\u003eDefense, and Advances (Di Nola).- Group Recommender Systems: Beyond Preferance Aggregation (Masthoff).- People-to-People Reciprocal Recommenders (Koprinska).- Natural Language Processing for Recommender Systems (Sar-Shalom).- Design and Evaluation of Cross-domain Recommender Systems (Cremonesi).- Part 3: Value and Impact of Recommender Systems.- Value and Impact of Recommender Sy\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003ePreface.- Introduction.- Part 1: General Recommendation Techniques.- Trust Your Neighbors: A Comprehensive Survey of Neighborhood-based Methods for Recommender Systems (Desrosiers).- Advances in Collaborative Filtering (Koren).- Item Recommendation from Implicit Feedback (Rendle).- Deep Learning for Recommender Systems (Zhang).- Context Aware Re commender Sytems : From Foundatiom to Recent Developments (Bauman).- Semantics and Content-based Recommendations (Musto).- Part 2: Special Recommendation 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Much of the content is based on the results of industrial research and development projects at Siemens.\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003e\u003cp\u003eData Analytics - Data and Relations - Data Preprocessing - Data Visualization - Correlation - Regression - Forecasting - Classification - Clustering.\u003c\/p\u003e","brand":"Springer Fachmedien Wiesbaden","offers":[{"title":"Default Title","offer_id":49372703490391,"sku":"9783658297787","price":999.99,"currency_code":"GBP","in_stock":false}]},{"product_id":"algorithms-for-data-science-9783319457956","title":"Algorithms for Data Science","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003eThis textbook on practical data analytics unites fundamental principles, algorithms, and data. Algorithms are the keystone of data analytics and the focal point of this textbook. Clear and intuitive explanations of the mathematical and statistical foundations make the algorithms transparent. But practical data analytics requires more than just the foundations. Problems and data are enormously variable and only the most elementary of algorithms can be used without modification. Programming fluency and experience with real and challenging data is indispensable and so the reader is immersed in Python and R and real data analysis. By the end of the book, the reader will have gained the ability to adapt algorithms to new problems and carry out innovative analyses.\u003cbr\u003eThis book has three parts:(a) Data Reduction: Begins with the concepts of data reduction, data maps, and information extraction. The second chapter introduces associative statistics, the mathematical foundation of scalable algorithms and distributed computing. Practical aspects of distributed computing is the subject of the Hadoop and MapReduce chapter.(b) Extracting Information from Data: Linear regression and data visualization are the principal topics of Part II. The authors dedicate a chapter to the critical domain of Healthcare Analytics for an extended example of practical data analytics. The algorithms and analytics will be of much interest to practitioners interested in utilizing the large and unwieldly data sets of the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System.(c) Predictive Analytics Two foundational and widely used algorithms, k-nearest neighbors and naive Bayes, are developed in detail. A chapter is dedicated to forecasting. The last chapter focuses on streaming data and uses publicly accessible data streams originating from the Twitter API and the NASDAQ stock market in the tutorials.\u003cbr\u003eThis book is intended for a one- or two-semester course in data analytics for upper-division undergraduate and graduate students in mathematics, statistics, and computer science. The prerequisites are kept low, and students with one or two courses in probability or statistics, an exposure to vectors and matrices, and a programming course will have no difficulty. The core material of every chapter is accessible to all with these prerequisites. The chapters often expand at the close with innovations of interest to practitioners of data science. Each chapter includes exercises of varying levels of difficulty. The text is eminently suitable for self-study and an exceptional resource for practitioners.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTrade Review\u003c\/h2\u003e\u003cp\u003e“This 430-page book contains an excellent collection of information on the subject of practical algorithms used in data science. The discussion of each algorithm starts with some basic concepts, followed by a tutorial with real datasets and detailed code examples in Python or R. Each chapter has a set of exercise problems so readers can practice the concepts learned in the chapter. … a good reference for practitioners, or a good textbook for graduate or upper-class undergraduate students.” (Xiannong Meng, Computing Reviews, September, 2017)\u003c\/p\u003e“This textbook on practical data analytics unites fundamental principles, algorithms, and data. … this book is devoted to upper-division undergraduate and graduate students in mathematics, statistics, and computer science. It is intended for a one- or two-semester course in data analytics and reflects the authors’ research experience in data science concepts and the teaching skills in various areas. … The text is eminently suitable for self-study and an exceptional resource for practitioners.” (Krzysztof J. Szajowski, zbMATH 1367.62005, 2017)  \u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003eIntroduction.- Data Mapping and Data Dictionaries.- Scalable Algorithms and Associative Statistics.- Hadoop and MapReduce.- Data Visualization.- Linear Regression Methods.- Healthcare Analytics.- Cluster Analysis.- k-Nearest Neighbor Prediction Functions.- The Multinomial Naive Bayes Prediction Function.- Forecasting.- Real-time Analytics.","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":49396273447255,"sku":"9783319457956","price":71.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783319457956.jpg?v=1730415325"},{"product_id":"machine-learning-9780323898591","title":"Machine Learning","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e","brand":"Elsevier Science \u0026 Technology","offers":[{"title":"Default Title","offer_id":49401782894935,"sku":"9780323898591","price":999.99,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780323898591.jpg?v=1730478507"},{"product_id":"ai-computing-systems-9780323953993","title":"AI Computing Systems","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e","brand":"Elsevier Science \u0026 Technology","offers":[{"title":"Default Title","offer_id":49401787482455,"sku":"9780323953993","price":999.99,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780323953993.jpg?v=1730478519"},{"product_id":"parametric-and-featurebased-cadcam-9780471002147","title":"Parametric and FeatureBased CadCAM","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003eThe book is the complete introduction and applications guide to this new technology. This book introduces the reader to features and gives an overview of geometric modeling techniques, discusses the conceptual development of features as modeling entities, illustrates the use of features for a variety of engineering design applications, and develops a set of broad functional requirements and addresses high level design issues.\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003eBACKGROUND.\u003cbr\u003e \u003cbr\u003e Geometric Modeling.\u003cbr\u003e \u003cbr\u003e FUNDAMENTALS.\u003cbr\u003e \u003cbr\u003e Feature Concepts.\u003cbr\u003e \u003cbr\u003e Feature Creation Techniques.\u003cbr\u003e \u003cbr\u003e APPLICATION OF FEATURES.\u003cbr\u003e \u003cbr\u003e Features in Design.\u003cbr\u003e \u003cbr\u003e Features in Manufacturing.\u003cbr\u003e \u003cbr\u003e Feature Mapping and Data Exchange.\u003cbr\u003e \u003cbr\u003e DESIGN AND IMPLEMENTATION.\u003cbr\u003e \u003cbr\u003e Design-by-Features Techniques.\u003cbr\u003e \u003cbr\u003e Feature Recognition Techniques.\u003cbr\u003e \u003cbr\u003e Implementation Tools.\u003cbr\u003e \u003cbr\u003e Feature-Based Process Planning.\u003cbr\u003e \u003cbr\u003e BEYOND FEATURES.\u003cbr\u003e \u003cbr\u003e Future CAD\/CAM Technologies.\u003cbr\u003e \u003cbr\u003e Appendices.\u003cbr\u003e \u003cbr\u003e Index.","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402475872599,"sku":"9780471002147","price":153.85,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780471002147.jpg?v=1730480518"},{"product_id":"engineering-of-mind-an-introduction-to-the-science-of-intelligent-systems-7-wiley-series-on-intelligent-systems-9780471438540","title":"Engineering of Mind An Introduction to the","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003eThis book covers the development of intelligent systems using a mixture of scientific, philosophical, and engineering concepts. It provides an expert blend of theory and practice in intelligent systems design and uses real-world examples to illustrate technical concepts.\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003ePreface.\u003cbr\u003e \u003cbr\u003e Emergence of a Theory.\u003cbr\u003e \u003cbr\u003e Knowledge.\u003cbr\u003e \u003cbr\u003e Perception.\u003cbr\u003e \u003cbr\u003e Goal Seeking and Planning.\u003cbr\u003e \u003cbr\u003e A Reference Model Architecture.\u003cbr\u003e \u003cbr\u003e Behavior Generation.\u003cbr\u003e \u003cbr\u003e World Modeling, Value Judgment, and Knowledge Representation.\u003cbr\u003e \u003cbr\u003e Sensory Processing.\u003cbr\u003e \u003cbr\u003e Engineering Unmanned Ground Vehicles.\u003cbr\u003e \u003cbr\u003e Future Possibilities.\u003cbr\u003e \u003cbr\u003e References.\u003cbr\u003e \u003cbr\u003e Index.","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402593509719,"sku":"9780471438540","price":131.35,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780471438540.jpg?v=1730480897"},{"product_id":"meme-architectures-knowledge-media-for-editing-distributing-and-managing-intellectual-resources-9780471453789","title":"Meme Architectures Knowledge Media for Editing","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003eProvides an integrated view of the five kinds of enabling technologies in terms of knowledge media architectures such as: multimedia and hypermedia, object oriented GUI and visual programming, reusable component software and component integration, network publishing and electronic commerce, and object oriented and multimedia databases.\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTrade Review\u003c\/h2\u003e\"…very interesting…recommended…\" (\u003ci\u003eE-Streams\u003c\/i\u003e, Vol. 7, No. 4)\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003e\u003cb\u003ePreface.\u003c\/b\u003e  \u003cp\u003e\u003cb\u003e1 Overview and Introduction.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Why Meme Media?\u003c\/p\u003e \u003cp\u003e1.2 How Do Meme Media Change the Reuse of Web Contents?\u003c\/p\u003e \u003cp\u003e1.3 How Do Meme Media Work?\u003c\/p\u003e \u003cp\u003e1.4 Frequently Asked Questions and Limitations.\u003c\/p\u003e \u003cp\u003e1.5 Organization of this Book.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Knowledge Media and Meme Media.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction to Knowledge Media and Meme Media.\u003c\/p\u003e \u003cp\u003e2.2 From Information Technologies to Media Technologies.\u003c\/p\u003e \u003cp\u003e2.3 Summary.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Augmentation Media Architectures and Technologies—A Brief Survey.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 History and Evolution of Augmentation Media.\u003c\/p\u003e \u003cp\u003e3.2 History and Evolution of Knowledge-Media Architectures.\u003c\/p\u003e \u003cp\u003e3.3 Meme Media and their Applications.\u003c\/p\u003e \u003cp\u003e3.4 Web Technologies and Meme Media.\u003c\/p\u003e \u003cp\u003e3.5 Summary.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 An Outline of IntelligentPad and Its Development History.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Brief Introduction to IntelligentPad.\u003c\/p\u003e \u003cp\u003e4.2 IntelligentPad Architecture.\u003c\/p\u003e \u003cp\u003e4.3 Worldwide Marketplace Architectures for Pads.\u003c\/p\u003e \u003cp\u003e4.4 End-User Computing and Media Toolkit System.\u003c\/p\u003e \u003cp\u003e4.5 Open Cross-Platform Reusability.\u003c\/p\u003e \u003cp\u003e4.6 Reediting and Redistribution by End-Users.\u003c\/p\u003e \u003cp\u003e4.7 Extension toward 3D Representation Media.\u003c\/p\u003e \u003cp\u003e4.8 Summary.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Object Orientation and MVC.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Object-Oriented System Architecture—A Technical Introduction.\u003c\/p\u003e \u003cp\u003e5.2 Class Refinement and Prototyping.\u003c\/p\u003e \u003cp\u003e5.3 Model, View, Controller.\u003c\/p\u003e \u003cp\u003e5.4 Window Systems and Event Dispatching.\u003c\/p\u003e \u003cp\u003e5.5 Summary.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Component Integration.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Object Reusability.\u003c\/p\u003e \u003cp\u003e6.2 Components and Application Linkage.\u003c\/p\u003e \u003cp\u003e6.3 Compound Documents and Object Embedding\/Linking.\u003c\/p\u003e \u003cp\u003e6.4 Generic Components.\u003c\/p\u003e \u003cp\u003e6.5 What to Reuse—Components or Sample Compositions?\u003c\/p\u003e \u003cp\u003e6.6 Reuses and Maintenance.\u003c\/p\u003e \u003cp\u003e6.7 Integration of Legacy Software.\u003c\/p\u003e \u003cp\u003e6.8 Distributed Component Integration and Web Technologies.\u003c\/p\u003e \u003cp\u003e6.9 Summary.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Meme Media Architecture.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Current Megatrends in Computer Systems.\u003c\/p\u003e \u003cp\u003e7.2 Primitive Media Objects.\u003c\/p\u003e \u003cp\u003e7.3 Composition through Slot Connections.\u003c\/p\u003e \u003cp\u003e7.4 Compound-Document Architecture.\u003c\/p\u003e \u003cp\u003e7.5 Standard Messages between Pads.\u003c\/p\u003e \u003cp\u003e7.6 Physical and Logical Events and their Dispatching.\u003c\/p\u003e \u003cp\u003e7.7 Save and Exchange Format.\u003c\/p\u003e \u003cp\u003e7.8 Copy and Shared Copy.\u003c\/p\u003e \u003cp\u003e7.9 Global Variable Pads.\u003c\/p\u003e \u003cp\u003e7.10 Summary.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Utilities for Meme Media.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Generic Utility Functions as Pads.\u003c\/p\u003e \u003cp\u003e8.2 FieldPad for the Event Sharing.\u003c\/p\u003e \u003cp\u003e8.3 StagePad for Programming User Operations.\u003c\/p\u003e \u003cp\u003e8.4 Geometrical Management of Pads.\u003c\/p\u003e \u003cp\u003e8.5 Proxy Pads to Assimilate External Objects.\u003c\/p\u003e \u003cp\u003e8.6 Legacy Software Migration.\u003c\/p\u003e \u003cp\u003e8.7 Special Effect Techniques.\u003c\/p\u003e \u003cp\u003e8.8 Expression Pad.\u003c\/p\u003e \u003cp\u003e8.9 Transformation Pads.\u003c\/p\u003e \u003cp\u003e8.10 Summary.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Multimedia Application Framework.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Component Pads for Multimedia Application Frameworks.\u003c\/p\u003e \u003cp\u003e9.2 Articulation of Objects.\u003c\/p\u003e \u003cp\u003e9.3 Hypermedia Framework.\u003c\/p\u003e \u003cp\u003e9.4 Summary.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 IntelligentPad and Databases.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Relational Databases, Object-Oriented Databases, and Instance Bases.\u003c\/p\u003e \u003cp\u003e10.2 Form Bases.\u003c\/p\u003e \u003cp\u003e10.3 Pads as Attribute Values.\u003c\/p\u003e \u003cp\u003e10.4 Multimedia Database.\u003c\/p\u003e \u003cp\u003e10.5 Hypermedia Database.\u003c\/p\u003e \u003cp\u003e10.6 Geographical Information Databases.\u003c\/p\u003e \u003cp\u003e10.7 Content-Based Search and Context-Based Search.\u003c\/p\u003e \u003cp\u003e10.8 Management and Retrieval of Pads.\u003c\/p\u003e \u003cp\u003e10.9 Summary.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Meme Pool Architectures.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Pad Publication Repository and the WWW.\u003c\/p\u003e \u003cp\u003e11.2 Pad Publication and Pad Migration.\u003c\/p\u003e \u003cp\u003e11.3 Web Pages as Pad Catalog.\u003c\/p\u003e \u003cp\u003e11.4 URL-Anchor Pads.\u003c\/p\u003e \u003cp\u003e11.5 HTMLViewerPad with Embedded Arbitrary Composite Pads.\u003c\/p\u003e \u003cp\u003e11.6 New Publication Media.\u003c\/p\u003e \u003cp\u003e11.7 Annotation on Web Pages.\u003c\/p\u003e \u003cp\u003e11.8 Piazza as a Meme Pool.\u003c\/p\u003e \u003cp\u003e11.9 Reediting and Redistributing Web Content as Meme Media Objects.\u003c\/p\u003e \u003cp\u003e11.10 Redistribution and Publication of Meme Media Objects as Web Content.\u003c\/p\u003e \u003cp\u003e11.11 Summary.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Electronic Commerce for Pads.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Electronic Commerce.\u003c\/p\u003e \u003cp\u003e12.2 From Pay-per-Copy to Pay-per-Use.\u003c\/p\u003e \u003cp\u003e12.3 Digital Accounting, Billing, and Payment.\u003c\/p\u003e \u003cp\u003e12.4 Ecology of Pads in the Market.\u003c\/p\u003e \u003cp\u003e12.5 Superdistribution of Pads.\u003c\/p\u003e \u003cp\u003e12.6 Pad Integration and Package Business.\u003c\/p\u003e \u003cp\u003e12.7 Summary.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Spatiotemporal Editing of Pads.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Geometrical Arrangement of Pads.\u003c\/p\u003e \u003cp\u003e13.2 Time-Based Arrangement of Pads.\u003c\/p\u003e \u003cp\u003e13.3 Spatiotemporal Editing of Pads.\u003c\/p\u003e \u003cp\u003e13.4 Information Visualization.\u003c\/p\u003e \u003cp\u003e13.5 Summary.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Dynamic Interoperability of Pads and Workflow Modeling.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Dynamic Interoperability of Pads Distributed across Networks.\u003c\/p\u003e \u003cp\u003e14.2 Extended Form-Flow System.\u003c\/p\u003e \u003cp\u003e14.3 Pad-Flow Systems.\u003c\/p\u003e \u003cp\u003e14.4 Dynamic Interoperability across Networks.\u003c\/p\u003e \u003cp\u003e14.5 Workflow and Concurrent Engineering.\u003c\/p\u003e \u003cp\u003e14.6 Summary.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Agent Media.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Three Different Meanings of Agents.\u003c\/p\u003e \u003cp\u003e15.2 Collaborative-and-Reactive Agents and Pads.\u003c\/p\u003e \u003cp\u003e15.3 Mobile Agents and Pads.\u003c\/p\u003e \u003cp\u003e15.4 Pad Migration and Script Languages.\u003c\/p\u003e \u003cp\u003e15.5 Summary.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Software Engineering with IntelligentPad.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 IntelligentPad as Middleware.\u003c\/p\u003e \u003cp\u003e16.2 Concurrent Engineering in Software Development.\u003c\/p\u003e \u003cp\u003e16.3 Components and Their Integration.\u003c\/p\u003e \u003cp\u003e16.4 Patterns and Frameworks in IntelligentPad.\u003c\/p\u003e \u003cp\u003e16.5 From Specifications to a Composite Pad.\u003c\/p\u003e \u003cp\u003e16.6 Pattern Specifications and the Reuse of Pads.\u003c\/p\u003e \u003cp\u003e16.7 IntelligentPad as a Software Development Framework.\u003c\/p\u003e \u003cp\u003e16.8 Summary.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Other Applications of IntelligentPad.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17.1 Capabilities Brought by the Implementation in IntelligentPad.\u003c\/p\u003e \u003cp\u003e17.2 Tool Integration Environments and Personal Information Management.\u003c\/p\u003e \u003cp\u003e17.3 Educational Applications.\u003c\/p\u003e \u003cp\u003e17.4 Web Page Authoring.\u003c\/p\u003e \u003cp\u003e17.5 Other Applications.\u003c\/p\u003e \u003cp\u003e17.6 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 3D Meme Media.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e18.1 3D Meme Media IntelligentBox.\u003c\/p\u003e \u003cp\u003e18.2 3D Application Systems.\u003c\/p\u003e \u003cp\u003e18.3 IntelligentBox Architecture.\u003c\/p\u003e \u003cp\u003e18.4 Example Boxes and Utility Boxes.\u003c\/p\u003e \u003cp\u003e18.5 Animation with IntelligentBox.\u003c\/p\u003e \u003cp\u003e18.6 Information Visualization with IntelligentBox.\u003c\/p\u003e \u003cp\u003e18.7 Component-Based Framework for Database Reification.\u003c\/p\u003e \u003cp\u003e18.8 Virtual Scientific Laboratory Framework.\u003c\/p\u003e \u003cp\u003e18.9 3D Meme Media and a Worldwide Repository of Boxes as a Meme Pool.\u003c\/p\u003e \u003cp\u003e18.10 Summary.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Organization and Access of Meme Media Objects.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e19.1 Organization and Access of Intellectual Resources.\u003c\/p\u003e \u003cp\u003e19.2 Topica Framework.\u003c\/p\u003e \u003cp\u003e19.3 The Application Horizon of the Topica Framework.\u003c\/p\u003e \u003cp\u003e19.4 Queries over the Web of Topica Documents.\u003c\/p\u003e \u003cp\u003e19.5 Related Research.\u003c\/p\u003e \u003cp\u003e19.6 Summary.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 IntelligentPad Consortium and Available Software.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e20.1 IntelligentPad Consortium.\u003c\/p\u003e \u003cp\u003e20.2 Available Software.\u003c\/p\u003e \u003cp\u003e20.3 Concluding Remarks.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAuthor Index.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSubject Index.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAbout the Author.\u003c\/b\u003e\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402597835095,"sku":"9780471453789","price":142.16,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780471453789.jpg?v=1730480913"},{"product_id":"modern-heuristic-search-methods-9780471962809","title":"Modern Heuristic Search Methods","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003eIncluding contributions from leading experts in the field, this book covers applications and developments of heuristic search methods for solving complex optimization problems. The book covers various local search strategies including genetic algorithms, simulated annealing, tabu search and hybrids thereof.\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003ePartial table of contents:\u003cbr\u003e \u003cbr\u003e Modern Heuristic Techniques.\u003cbr\u003e \u003cbr\u003e TECHNIQUES.\u003cbr\u003e \u003cbr\u003e Localized Simulated Annealing in Constraint Satisfaction andOptimization.\u003cbr\u003e \u003cbr\u003e Observing Logical Interdependencies in Tabu Search: Methods andResults.\u003cbr\u003e \u003cbr\u003e Reactive Search: Toward Self-Tuning Heuristics.\u003cbr\u003e \u003cbr\u003e Integrating Local Search into Genetic Algorithms.\u003cbr\u003e \u003cbr\u003e CASE STUDIES.\u003cbr\u003e \u003cbr\u003e Local Search for Steiner Trees in Graphs.\u003cbr\u003e \u003cbr\u003e Local Search Strategies for the Vehicle Fleet Mix Problem.\u003cbr\u003e \u003cbr\u003e A Tabu Search Algorithm for Some Discrete-Continuous SchedulingProblems.\u003cbr\u003e \u003cbr\u003e The Analysis of Waste Flow Data from Multi-Unit IndustrialComplexes Using Genetic Algorithms.\u003cbr\u003e \u003cbr\u003e The Evolution of Solid Object Designs Using GeneticAlgorithms.\u003cbr\u003e \u003cbr\u003e The Convoy Movement Problem with Initial Delays.\u003cbr\u003e \u003cbr\u003e A Brief Comparison of Some Evolutionary Optimization Methods.\u003cbr\u003e \u003cbr\u003e Index.","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402695713111,"sku":"9780471962809","price":172.76,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780471962809.jpg?v=1730481264"},{"product_id":"retargetable-compilers-for-embedded-core-processors-methods-and-experiences-in-industrial-applications-9780792399599","title":"Retargetable Compilers for Embedded Core","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003e\u003cbr\u003e  \u003cem\u003eRetargetable Compilers for Embedded Core Processors\u003c\/em\u003e, with a  Foreword written by Ahmed Jerraya and Pierre Paulin, overviews the  techniques of modern retargetable compilers and shows the application  of practical techniques to embedded instruction-set processors.\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003eList of Figures. List of Tables. Foreword. Preface. 1.  Introduction. 2. An Overview of Compiler Techniques for Embedded  Processors. 3. Two Emerging Approaches: Model-based and  Rule-driven. 4. Practical Issues in Compiler Design for Embedded  Processors. 5. Compiler Transformations for DSP Address  Calculation. 6. Pushing the Capabilities of Compiler  Methodologies in Industry. 7. Tools for Instruction-Set Design  and Redesign. 8. Conclusion. Bibliography. Glossary of  Abbreviations. Index.","brand":"Springer","offers":[{"title":"Default Title","offer_id":49405068771671,"sku":"9780792399599","price":999.99,"currency_code":"GBP","in_stock":false}]},{"product_id":"cognitive-internet-of-things-collaboration-to-optimize-action-9781137594655","title":"Cognitive Internet of Things Collaboration to","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Introduction\u003cbr\u003e\u003cbr\u003e2. What is a Cognitive Device?\u003cbr\u003e\u003cbr\u003e3. Cognitive Devices as Human Assistants\u003cbr\u003e\u003cbr\u003e4. Cognitive Things in an Organization\u003cbr\u003e\u003cbr\u003e5. Reuse and Monetization\u003cbr\u003e\u003cbr\u003e6. Intelligent Observations\u003cbr\u003e\u003cbr\u003e7. Organization of Knowledge and Problem Solving\u003cbr\u003e\u003cbr\u003e8. Installation, Training, Maintenance, Security, and Infrastructure\u003cbr\u003e\u003cbr\u003e9. Machine-to-Machine Interfaces\u003cbr\u003e\u003cbr\u003e10. Man-to-Machine Interfaces\u003cbr\u003e\u003cbr\u003e11. Assisting in Human Communications\u003cbr\u003e\u003cbr\u003e12. Balance of Power and Societal Impacts\u003cbr\u003e","brand":"Palgrave Macmillan","offers":[{"title":"Default Title","offer_id":49407207145815,"sku":"9781137594655","price":28.12,"currency_code":"GBP","in_stock":false}]},{"product_id":"encyclopedia-of-database-systems-9781461482666","title":"Encyclopedia of Database Systems","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003e.NET Remoting.- Absolute Time.- Abstract Versus Concrete Temporal Query Languages.- Abstraction.- Access Control.- Access Control Administration Policies.- Access Control Policy Languages.- Access Path.- ACID Properties.- Active and Real-Time Data Warehousing.- Active Database Coupling Modes.- Active Database Execution Model.- Active Database Knowledge Model.- Active Database Management System Architecture.- Active Database Rulebase.- Active Database, Active Database (Management) System.- Active Storage.- Active XML.- Activity.- Activity Diagrams.- Actors\/Agents\/Roles.- Adaptive Interfaces.- Adaptive Middleware for Message Queuing Systems.- Adaptive Query Processing.- Adaptive Stream Processing.- ADBMS.- Administration Model for RBAC.- Administration Wizards.- Advanced Information Retrieval Measures.- Aggregation: Expressiveness and Containment.- Aggregation-Based Structured Text Retrieval.- Air Indexes for Spatial Databases.- AJAX.- Allen's Relations.- AMOSQL.- AMS Sketch.- Anchor Tex\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003e.NET Remoting.- Absolute Time.- Abstract Versus Concrete Temporal Query Languages.- Abstraction.- Access Control.- Access Control Administration Policies.- Access Control Policy Languages.- Access Path.- ACID Properties.- Active and Real-Time Data Warehousing.- Active Database Coupling Modes.- Active Database Execution Model.- Active Database Knowledge Model.- Active Database Management System Architecture.- Active Database Rulebase.- Active Database, Active Database (Management) System.- Active Storage.- Active XML.- Activity.- Activity Diagrams.- Actors\/Agents\/Roles.- Adaptive Interfaces.- Adaptive Middleware for Message Queuing Systems.- Adaptive Query Processing.- Adaptive Stream Processing.- ADBMS.- Administration Model for RBAC.- Administration Wizards.- Advanced Information Retrieval Measures.- Aggregation: Expressiveness and Containment.- Aggregation-Based Structured Text Retrieval.- Air Indexes for Spatial Databases.- AJAX.- Allen's Relations.- AMOSQL.- AMS Sketch.- Anchor Text.- Annotation.- Annotation-based Image Retrieval.- Anomaly Detection on Streams.- Anonymity.- ANSI\/INCITS RBAC Standard.- Answering Queries Using Views.- Anti-monotone Constraints.- Applicability Period.- Application Benchmark.- Application Recovery.- Application Server.- Application-Level Tuning.- Applications of Emerging Patterns for Microarray Gene Expression Data Analysis.- Applications of Sensor Network Data Management.- Approximate Queries in Peer-to-Peer Systems.- Approximate Query Processing.- Approximate Reasoning.- Approximation of Frequent Itemsets.- Apriori Property and Breadth-First Search Algorithms.- Architecture-Conscious Database System.- Archiving Experimental Data.- Armstrong Axioms.- Array Databases.- Array Databases_old.- Association Rule Mining on Streams.- Association Rules.- Asymmetric Encryption.- Atelic Data.- Atomic Event.- Atomicity.- Audio.- Audio Classification.- Audio Content Analysis.- Audio Metadata.- Audio Representation.- Audio Segmentation.- Auditing and Forensic Analysis.- Authentication.- Automatic Image Annotation.- Autonomous Replication.- Average Precision.- Average Precision at n.- Average Precision Histogram.- Average R-Precision.- B+-Tree.- Backup and Restore.- Bag Semantics.- Bagging.- Bayesian Classification.- Benchmark Frameworks.- Benchmarks for Big Data Analytics.- Big Data Platforms for Data Analytics.- Big Stream Systems.- Biological Metadata Management.- Biological Networks.- Biological Resource Discovery.- Biological Sequences.- Biomedical Data\/Content Acquisition, Curation.- Biomedical Image Data Types and Processing.- Biomedical Scientific Textual Data Types and Processing.- Biostatistics and Data Analysis.- Bi-Temporal Indexing.- Bitemporal Interval.- Bitemporal Relation.- Bitmap Index.- Bitmap-based Index Structures.- Blind Signatures.- Bloom Filters.- BM25.- Boolean Model.- Boosting.- Bootstrap.- Boyce-Codd Normal Form.- BP-Completeness.- Bpref.- Browsing.- Browsing in Digital Libraries.- B-Tree Locking.- Buffer Management.- Buffer Manager.- Buffer Pool.- Business Intelligence.- Business Process Execution Language.- Business Process Management.- Business Process Modeling Notation.- Business Process Reengineering.- Cache-Conscious Query Processing.- Calendar.- Calendric System.- CAP Theorem.- Cardinal Direction Relationships.- Cartesian Product.- Cataloging in Digital Libraries.- Causal Consistency.- Certain (and Possible) Answers.- Change Detection on Streams.- Channel-Based Publish\/Subscribe.- Chart.- Chase.- Checksum and Cyclic Redundancy Check Mechanism.- Choreography.- Chronon.- Citation.- Classification.- Classification by Association Rule Analysis.- Classification in Streams.- Client-Server Architecture.- Clinical Data Acquisition, Storage and Management.- Clinical Data and Information Models.- Clinical Data Quality and Validation.- Clinical Decision Support.- Clinical Document Architecture.- Clinical Event.- Clinical Knowledge Repository.- Clinical Observation.- Clinical Ontologies.- Clinical Order.- Closed Itemset Mining and Non-redundant Association Rule Mining.- Closest-Pair Query.- Cloud Computing.- Cloud Intelligence.- Cluster and Distance Measure.- Clustering for Post Hoc Information Retrieval.- Clustering on Streams.- Clustering Overview and Applications.- Clustering Validity.- Clustering with Constraints.- Collaborative Filtering.- Column Segmentation.- Column Stores.- Common Warehouse Metamodel.- Comparative Visualization.- Compensating Transactions.- Complex Event.- Complex Event Processing.- Composed Services and WS-BPEL.- Composite Event.- Composition.- Comprehensions.- Compression of Mobile Location Data.- Computational Media Aesthetics.- Computationally Complete Relational Query Languages.- Computerized Physician Order Entry.- Conceptual Modeling Foundations.- Conceptual Schema Design.- Concurrency Control - Traditional Approaches.- Concurrency Control for Replicated Databases.- Concurrency Control Manager.- Conditional Tables.- Conjunctive Query.- Connection.- Consistency Models For Replicated Data.- Consistent Query Answering.- Constraint Databases.- Constraint Query Languages.- Constraint-Driven Database Repair.- Content-and-Structure Query.- Content-Based Publish\/Subscribe.- Content-Based Video Retrieval.- Content-Only Query.- Context.- Contextualization in Structured Text Retrieval.- Continuous Data Protection.- Continuous Monitoring of Spatial Queries.- Continuous Multimedia Data Retrieval.- Continuous Queries in Sensor Networks.- Continuous Query.- ConTract.- Control Data.- Convertible Constraints.- Coordination.- Copyright Issues in Databases.- CORBA.- Correctness Criteria Beyond Serializability.- Cost and quality trade-offs in crowdsourcing.- Cost Estimation.- Count-Min Sketch.- Coupling and De-coupling.- Covering Index.- Crash Recovery.- Cross-Language Mining and Retrieval.- Cross-Modal Multimedia Information Retrieval.- Cross-Validation.- Crowd Database Operators.- Crowd Database Systems.- Crowd Mining and Analysis.- Crowdsourcing Geographic Information Systems.- Cube.- Cube Implementations.- Current Semantics.- Curse of Dimensionality.- Daplex.- Data Acquisition and Dissemination in Sensor Networks.- Data Aggregation in Sensor Networks.- Data Broadcasting, Caching and Replication in Mobile Computing.- Data Cleaning.- Data Compression in Sensor Networks.- Data Conflicts.- Data Definition.- Data Definition Language (DDL).- Data Dictionary.- Data Encryption.- Data Estimation in Sensor Networks.- Data Exchange.- Data Fusion.- Data Fusion in Sensor Networks.- Data Generation.- Data Governance.- Data Integration Architectures and Methodology for the Life Sciences.- Data Integration in Web Data Extraction System.- Data Management for VANETs.- Data Management Fundamentals: Database Management System.- Data Management in Data Centers.- Data Manipulation.- Data Manipulation Language (DML).- Data Mart.- Data Migration Management.- Data Mining.- Data Partitioning.- Data Privacy and Patient Consent.- Data Profiling.- Data Provenance.- Data Quality Assessment.- Data Quality Dimensions.- Data Quality Models.- Data Rank\/Swapping.- Data Reduction.- Data Replication.- Data Sampling.- Data Scrubbing.- Data Sketch\/Synopsis.- Data Skew.- Data Storage and Indexing in Sensor Networks.- Data Stream.- Data Stream Management Architectures and Prototypes.- Data Types in Scientific Data Management.- Data Uncertainty Management in Sensor Networks.- Data Visualization.- Data Warehouse.- Data Warehouse Life-Cycle and Design.- Data Warehouse Maintenance, Evolution and Versioning.- Data Warehouse Metadata.- Data Warehouse Security.- Data Warehousing for Clinical Research.- Data Warehousing in Cloud Environments.- Data Warehousing on Non-Conventional Data.- Data Warehousing Systems: Foundations and Architectures.- Data, Text, and Web Mining in Healthcare.- Database.- Database Adapter and Connector.- Database Administrator (DBA).- Database Appliances.- Database Benchmarks.- Database Clustering Methods.- Database Clusters.- Database Dependencies.- Database Design.- Database Languages for Sensor Networks.- Database Machine.- Database Management System.- Database Middleware.- Database Repair.- Database Reverse Engineering.- Database Schema.- Database Security.- Database System.- Database Techniques to Improve Scientific Simulations.- Database Trigger.- Database Tuning using Combinatorial Search.- Database Tuning using Online Algorithms.- Database Tuning using Trade-off Elimination.- Database Use in Science Applications.- Datalog.- DBMS Component.- DBMS Interface.- DCE.- DCOM.- Decay Models.- Decision Rule Mining in Rough Set Theory.- Decision Tree Classification.- Decision Trees.- Declarative Networking.- Deductive Data Mining using Granular Computing.- Deduplication.- Deduplication in Data Cleaning.- Deep Instantiation.- Deep-Web Search.- Dense Index.- Dense Pixel Displays.- Density-based Clustering.- Description Logics.- Design for Data Quality.- Dewey Decimal System.- Diagram.- Difference.- Differential Privacy.- Digital Archives and Preservation.- Digital Curation.- Digital Elevation Models.- Digital Libraries.- Digital Rights Management.- Digital Signatures.- Dimension.- Dimension Reduction Techniques for Clustering.- Dimensionality Reduction.- Dimensionality Reduction Techniques For Nearest Neighbor Computations.- Dimension-Extended Topological Relationships.- Direct Attached Storage.- Direct Manipulation.- Disaster Recovery.- Disclosure Risk.- Discounted Cumulated Gain.- Discovery.- Discrete Wavelet Transform and Wavelet Synopses.- Discretionary Access Control.- Disk.- Disk Power Saving.- Distortion Techniques.- Distributed Architecture.- Distributed Concurrency Control.- Distributed Data Streams.- Distributed Database Design.- Distributed Database Systems.- Distributed DBMS.- Distributed Deadlock Management.- Distributed File Systems.- Distributed Hash Table.- Distributed Join.- Distributed Machine Learning.- Distributed Query Optimization.- Distributed Query Processing.- Distributed Recovery.- Distributed Spatial Databases.- Distributed Transaction Management.- Divergence from Randomness Models.- D-measure.- Document.- Document Clustering.- Document Databases.- Document Field.- Document Length Normalization.- Document Links and Hyperlinks.- Document Representations (Inclusive Native and Relational).- Dublin Core.- Dynamic Graphics.- Dynamic Web Pages.- eAccessibility.- ECA Rule Action.- ECA Rule Condition.- ECA Rules.- e-Commerce Transactions.- Effectiveness Involving Multiple Queries.- Ehrenfeucht-Fraïssé Games.- Elasticity.- Electronic Dictionary.- Electronic Encyclopedia.- Electronic Health Record.- Electronic Ink Indexing.- Electronic Newspapers.- Eleven Point Precision-recall Curve.- Emergent Semantics.- Emerging Pattern Based Classification.- Emerging Patterns.- Energy Efficiency in Data Centers.- Ensemble.- Enterprise Application Integration.- Enterprise Content Management.- Enterprise Service Bus.- Enterprise Terminology Services.- Entity Relationship Model.- Entity Resolution.- Entity Retrieval.- Equality-Generating Dependencies.- ERR- Expected Reciprocal Rank.- ERR-IA Intent-aware ERR.- Escrow Transactions.- European Law in Databases.- Evaluation Metrics for Structured Text Retrieval.- Evaluation of Relational Operators.- Event.- Event and Pattern Detection over Streams.- Event Causality.- Event Channel.- Event Cloud.- Event Detection.- Event Driven Architecture.- Event Flow.- Event in Active Databases.- Event in Temporal Databases.- Event Lineage.- Event Pattern Detection.- Event Prediction.- Event Processing Agent.- Event Processing Network.- Event Sink.- Event Source.- Event Specification.- Event Stream.- Event Transformation.- Event-Driven Business Process Management.- Eventual Consistency.- Evidence Based Medicine.- Executable Knowledge.- Execution Skew.- Explicit Event.- Exploratory Data Analysis.- Expressive Power of Query Languages.- Extended Entity-Relationship Model.- Extended Transaction Models and the ACTA Framework.- Extendible Hashing.- Extraction, Transformation, and Loading.- Faceted Search.- Fault-Tolerance and High Availability in Data Stream Management Systems.- Feature Extraction for Content-Based Image Retrieval.- Feature Selection for Clustering.- Feature-Based 3D Object Retrieval.- Field-Based Information Retrieval Models.- Field-Based Spatial Modeling.- First-Order Logic: Semantics.- First-Order Logic: Syntax.- Fixed Time Span.- Flex Transactions.- FM Synopsis.- F-Measure.- Focused Web Crawling.- FOL Modeling of Integrity Constraints (Dependencies).- Forever.- Form.- Fourth Normal Form.- FQL.- Fractal.- Frequency Moments.- Frequent Graph Patterns.- Frequent Items on Streams.- Frequent Itemset Mining with Constraints.- Frequent Itemsets and Association Rules.- Frequent Partial Orders.- Fully-Automatic Web Data Extraction.- Functional Data Model.- Functional Dependencies for Semi-Structured Data.- Functional Dependency.- Functional Query Language.- Fuzzy Models.- Fuzzy Relation.- Fuzzy Set.- Fuzzy Set Approach.- Fuzzy\/Linguistic IF-THEN Rules and Linguistic Descriptions.- Gazetteers.- Gene Expression Arrays.- Generalization of ACID Properties.- Generalized Search Tree.- Genetic Algorithms.- Geographic Information System.- Geographical Information Retrieval.- Geography Markup Language.- Geometric Stream Mining.- GEO-RBAC Model.- Georeferencing.- Geosocial Networks.- Geospatial Metadata.- Geo-Targeted Web Search.- GMAP.- Grammar Inference.- Graph.- Graph Data Management in Scientific Applications.- Graph Database.- Graph Management in the Life Sciences.- Graph Mining.- Graph Mining on Streams.- Graph OLAP.- Graphical Models for Uncertain Data Management.- Grid and Workflows.- Grid File (and Family).- GUIs for Web Data Extraction.- Hash Functions.- Hash Join.- Hash-based Indexing.- Healthcare Metrics.- Hierarchial Clustering.- Hierarchical Data Model.- Hierarchical Data Summarization.- Hierarchical Heavy Hitter Mining on Streams.- Hierarchy.- High Dimensional Indexing.- Histogram.- Histograms on Streams.- History in Temporal Databases.- Homomorphic Encryption.- Horizontally Partitioned Data.- Human Factors Modeling in Crowdsourcing.- Human-centered Computing: Application to Multimedia.- Human-Computer Interaction.- Hypertexts.- I\/O Model of Computation.- Icon.- Iconic Displays.- Image.- Image Content Modeling.- Image Database.- Image Management for Biological Data.- Image Metadata.- Image Querying.- Image Representation.- Image Retrieval and Relevance Feedback.- Image Segmentation.- Image Similarity.- Implementation of Database Operators (Joins, Group by, etc.).- Implication of Constraints.- Implications of Genomics for Clinical Informatics.- Implicit Event.- Incomplete Information.- Inconsistent Databases.- Incremental Computation of Queries.- Incremental Crawling.- Incremental Maintenance of Views with Aggregates.- Index Creation and File Structures.- Index Join.- Index Structures for Biological Sequences.- Index Tuning.- Indexed Sequential Access Method.- Indexing and Similarity Search.- Indexing Compressed Text.- Indexing Historical Spatio-Temporal Data.- Indexing in pub\/sub systems.- Indexing Metric Spaces.- Indexing of Data Warehouses.- Indexing of the Current and Near-Future Positions of Moving Objects.- Indexing Techniques for Multimedia Data Retrieval.- Indexing the Web.- Indexing Uncertain Data.- Indexing Units of Structured Text Retrieval.- Indexing with Crowds.- Individually Identifiable Data.- Inference Control in Statistical Databases.- Information Extraction.- Information Filtering.- Information Foraging.- Information Integration.- Information Integration Techniques for Scientific Data.- Information Lifecycle Management.- Information Loss Measures.- Information Navigation.- Information Quality.- Information Quality and Decision Making.- Information Quality Assessment.- Information Quality Policy and Strategy.- Information Quality: Managing Information as a Product.- Information Retrieval.- Information Retrieval Models.- Information Retrieval Operations.- Infrastructure As-A-Service (IaaS).- Initiative for the Evaluation of XML Retrieval.- Initiator.- In-Network Query Processing.- Integrated DB and IR Approaches.- Integration of Rules and Ontologies.- Intelligent Storage Systems.- Interactive Analytics in Social Media.- Interface.- Interface Engines in Healthcare.- Interoperability in Data Warehouses.- Interoperation of NLP-based Systems with Clinical Databases.- Inter-Operator Parallelism.- Inter-Query Parallelism.- Intra-operator Parallelism.- Intra-Query Parallelism.- Intrusion Detection Technology.- Inverse Document Frequency.- Inverted Files.- IP Storage.- Iterator.- Java Database Connectivity.- Java Enterprise Edition.- Java Metadata Facility.- Join.- Join Dependency.- Join Index.- Join Order.- k-Anonymity.- Karp-Luby Sampling.- KDD Pipeline.- Key.- K-Means and K-Medoids.- Knowledge Base.- Knowledge Base Extraction.- Language Models.- Languages for Web Data Extraction.- Learning Distance Measures.- Lexical Analysis of Textual Data.- Licensing and Contracting Issues in Databases.- Lifespan.- Lightweight Ontologies.- Linear Hashing.- Linear Regression.- Linked Open Data.- Linking and Brushing.- Load Balancing in Peer-to-Peer Overlay Networks.- Load Shedding.- LOC METS.- Locality.- Locality of Queries.- Location Based Recommendation.- Location Management in Mobile Environments.- Location Update Management.- Location-Based Services.- Locking Granularity and Lock Types.- Logging and Recovery.- Logging\/Recovery Subsystem.- Logical and Physical Data Independence.- Logical Database Design: from Conceptual to Logical Schema.- Logical Document Structure.- Logical Foundations of Web Data Extraction.- Logical Models of Information Retrieval.- Logical Unit Number.- Logical Unit Number Mapping.- Logical Volume Manager.- Log-Linear Regression.- Loop.- Loose Coupling.- Machine Learning in Computational Biology.- Main Memory.- Main Memory DBMS.- Maintenance of Materialized Views with Outer-Joins.- Maintenance of Recursive Views.- Managing Compressed Structured Text.- Managing Data Integration Uncertainty.- Managing Probabilistic Entity Extraction.- Mandatory Access Control.- MANET Databases.- MAP.- Map Matching.- MapReduce.- Markup Language.- MashUp.- Massive Array of Idle Disks.- Matrix Masking.- Max-Pattern Mining.- Mean Reciprocal Rank.- Measure.- Mediation.- Membership Query.- Memory Hierarchy.- Memory Locality.- Merkle Trees.- Message Authentication Codes.- Message Queuing Systems.- Meta Data Repository.- Meta Object Facility.- Metadata.- Metadata Interchange Specification.- Metadata Registry, ISO\/IEC 11179.- Metamodel.- Metasearch Engines.- Metric Space.- Microaggregation.- Microbenchmark.- Microdata.- Microdata Rounding.- Middleware Support for Database Replication and Caching.- Middleware Support for Precise Failure Semantics.- Mining of Chemical Data.- Mobile Database.- Mobile Interfaces.- Mobile resource search.- Mobile Sensor Network Data Management.- Model Management.- Model-based Querying in Sensor Networks.- Monotone Constraints.- Monte Carlo Methods for Uncertain Data.- Moving Object.- Moving Objects Databases and Tracking.- MRR.- Multi-Data Center Consistency Properties.- Multi-Data Center Replication Protocols.- Multidimensional Data Formats.- Multidimensional Modeling.- Multidimensional Scaling.- Multi-Level Modeling.- Multi-Level Recovery and the ARIES Algorithm.- Multilevel Secure Database Management System.- Multilevel Transactions and Object-Model Transactions.- Multimedia Data.- Multimedia Data Buffering.- Multimedia Data Indexing.- Multimedia Data Querying.- Multimedia Data Storage.- Multimedia Databases.- Multimedia Information Retrieval Model.- Multimedia Metadata.- Multimedia Presentation Databases.- Multimedia Resource Scheduling.- Multimedia Retrieval Evaluation.- Multimedia Tagging.- Multimodal Interfaces.- Multi-Pathing.- Multiple Representation Modeling.- Multi-Query Optimization.- Multi-Resolution Terrain Modeling.- Multi-Step Query Processing.- Multitenancy.- Multi-Tier Architecture.- Multi-tier Storage Systems.- Multivalued Dependency.- Multivariate Visualization Methods.- Multi-version Serializability and Concurrency Control.- Naive Tables.- Narrowed Extended XPath I.- Natural Interaction.- Near-duplicate Retrieval.- Nearest Neighbor Classification.- Nearest Neighbor Query.- Nearest Neighbor Query in Spatio-temporal Databases.- Nested Loop Join.- Nested Transaction Models.- Network Attached Secure Device.- Network Attached Storage.- Network Data Model.- Neural Networks.- N-Gram Models.- Noise Addition.- Nonparametric Data Reduction Techniques.- Non-Perturbative Masking Methods.- Non-relational Streams.- Nonsequenced Semantics.- Normal Form ORA-SS Schema Diagrams.- Normal Forms and Normalization.- NoSQL Stores.- Now in Temporal Databases.- Null Values.- OASIS.- Object Constraint Language.- Object Data Models.- Object Identity.- Object Recognition.- Object Relationship Attribute Data Model for Semi-structured Data.- Object Storage Protocol.- Object-Role Modeling.- OLAM.- OLAP Personalization and Recommendation.- OLAP Personalization and Recommendation_old.- One-Copy-Serializability.- One-Pass Algorithm.- On-Line Analytical Processing.- Online Recovery in Parallel Database Systems.- Ontologies and Life Science Data Management.- Ontology.- Ontology Elicitation.- Ontology Engineering.- Ontology Visual Querying.- Ontology-Based Data Access and Integration.- Open Database Connectivity.- Open Information Extraction.- Open Nested Transaction Models.- Operator-Level Parallelism.- Opinion Mining.- Optimistic Replication and Resolution.- Optimization and Tuning in Data Warehouses.- OQL.- Orchestration.- Order Dependency.- OR-Join.- OR-Split.- OSQL.- Outlier Detection.- Overlay Network.- OWL: Web Ontology Language.- P\/FDM.- Parallel and Distributed Data Warehouses.- Parallel Coordinates.- Parallel Data Placement.- Parallel Database Management.- Parallel Hash Join, Parallel Merge Join, Parallel Nested Loops Join.- Parallel Query Execution Algorithms.- Parallel Query Optimization.- Parallel Query Processing.- Parameterized Complexity of Queries.- Parametric Data Reduction Techniques.- Partial Replication.- Path Query.- Pattern-Growth Methods.- Peer Data Management System.- Peer to Peer Overlay Networks: Structure, Routing and Maintenance.- Peer-To-Peer Content Distribution.- Peer-to-Peer Data Integration.- Peer-to-Peer Publish-Subscribe Systems.- Peer-to-Peer Storage.- Peer-to-Peer System.- Peer-to-Peer Web Search.- Performance Analysis of Transaction Processing Systems.- Performance Monitoring Tools.- Period-Stamped Temporal Models.- Personalized Web Search.- Petri Nets.- Physical Clock.- Physical Database Design for Relational Databases.- Physical Layer Tuning.- Pipeline.- Pipelining.- Platform As-A-Service (PaaS).- Point-in-Time Copy.- Point-Stamped Temporal Models.- Polytransactions.- Positive Relational Algebra.- Possible Answers.- PRAM.- Precision.- Precision and Recall.- Precision at n.- Precision-Oriented Effectiveness Measures.- Predictive Analytics.- Preference Queries.- Preference Specification.- Prescriptive Analytics.- Presenting Structured Text Retrieval Results.- Primary Index.- Principal Component Analysis.- Privacy.- Privacy Metrics.- Privacy Policies and Preferences.- Privacy through Accountability.- Privacy-Enhancing Technologies.- Privacy-Preserving Data Mining.- Privacy-Preserving DBMSs.- Private Information Retrieval.- Probabilistic Databases.- Probabilistic Entity Resolution.- Probabilistic Retrieval Models and Binary Independence Retrieval (BIR) Model.- Probabilistic Skylines.- Probabilistic Spatial Queries.- Probabilistic Temporal Databases.- Probability Ranking Principle.- Probability Smoothing.- Process Life Cycle.- Process Mining.- Process Modeling.- Process Optimization.- Process Structure of a DBMS.- Processing Overlaps in Structured Text Retrieval.- Processing Structural Constraints.- Processor Cache.- Profiles and Context for Structured Text Retrieval.- Projection.- Propagation-based Structured Text Retrieval.- Protection from Insider Threats.- Provenance.- Provenance and Reproducibility.- Provenance in Databases.- Provenance in Scientific Databases.- Provenance in Workflows.- Provenance Management.- Provenance Standards.- Provenance Storage.- Provenance: Privacy and Security.- Pseudonymity.- Publish\/Subscribe.- Publish\/Subscribe over Streams.- Punctuations.- Q-measure.- Quadtrees (and Family).- Qualitative Temporal Reasoning.- Quality and Trust of Information Content and Credentialing.- Quality of Data Warehouses.- Quantiles on Streams.- Quantitative Association Rules.- QUEL.- Query by Humming.- Query Containment.- Query Evaluation Techniques for Multidimensional Data.- Query Expansion for Information Retrieval.- Query Expansion Models.- Query Language.- Query Languages and Evaluation Techniques for Biological Sequence Data.- Query Languages for the Life Sciences.- Query Load Balancing in Parallel Database Systems.- Query Optimization.- Query Optimization (in Relational Databases).- Query Optimization in Sensor Networks.- Query Plan.- Query Point Movement Techniques for Content-Based Image Retrieval.- Query Processing.- Query Processing (in Relational Databases).- Query Processing and Optimization in Object Relational Databases.- Query Processing in data integration systems.- Query Processing in Data Warehouses.- Query Processing in Deductive Databases.- Query Processing over Uncertain Data.- Query Processor.- Query Rewriting.- Query Rewriting Using Views.- Query Translation.- Quorum Systems.- Randomization Methods to Ensure Data Privacy.- Range Query.- Rank-aware Query Processing.- Ranked XML Processing.- Ranking Functions.- Ranking Views.- Rank-Join.- Rank-Join Indices.- Raster Data Management and Multi-Dimensional Arrays.- RDF Stores.- RDF Technology.- Real and Synthetic Test Datasets.- Real-Time Transaction Processing.- Recall.- Receiver Operating Characteristic.- Recommender Systems.- Record Linkage.- Record Matching.- Redundant Arrays of Independent Disks.- Reference Knowledge.- Region Algebra.- Regulatory Compliance in Data Management.- Relational Algebra.- Relational Calculus.- Relational Model.- Relationships in Structured Text Retrieval.- Relative Time.- Relevance.- Relevance Feedback.- Relevance Feedback for Content-Based Information Retrieval.- Relevance Feedback for Text Retrieval.- Replica Control.- Replica Freshness.- Replicated Data Types.- Replicated Database Concurrency Control.- Replication.- Replication Based on Group Communication.- Replication for Availability and Fault-Tolerance.- Replication for High Availability.- Replication for Paxos.- Replication for Scalability.- Replication in Multi-Tier Architectures.- Replication with Snapshot Isolation.- Reputation and Trust.- Request Broker.- Residuated Lattice.- Resource Allocation Problems in Spatial Databases.- Resource Description Framework.- Resource Description Framework (RDF) Schema (RDFS).- Resource Identifier.- Result Display.- Retrospective Event Processing.- Reverse Nearest Neighbor Query.- Reverse Top-k Queries.- Rewriting Queries using Views.- RMI.- Road Networks.- Rocchio's Formula.- Role Based Access Control.- R-Precision.- R-Tree (and Family).- Rule-based Classification.- Safety and Domain Independence.- Sagas.- Sampling Techniques for Statistical Databases.- SAN File System.- Scalable Decision Tree Construction.- Scheduler.- Scheduling Strategies for Data Stream Processing.- Schema Evolution.- Schema Mapping.- Schema Mapping Composition.- Schema Matching.- Schema Tuning.- Schema Versioning.- Scheme\/Ontology Extraction.- Scientific Databases.- Scientific Visualization.- Scientific Workflows.- Score Aggregation.- Screen Scraper.- SCSI Target.- SDC Score.- Search Engine Metrics.- Searching Digital Libraries.- Second Normal Form (2NF).- Secondary Index.- Secure Data Outsourcing.- Secure Database Development.- Secure Multiparty Computation Methods.- Secure Transaction Processing.- Security Services.- Segmentation and Stratification.- Segmentation and Stratification_old.- Selection.- Selectivity Estimation.- Self-Maintenance of Views.- Self-Management Technology in Databases.- Semantic Atomicity.- Semantic Crowd Sourcing.- Semantic Data Integration for Life Science Entities.- Semantic Data Model.- Semantic Matching.- Semantic Modeling and Knowledge Representation for Multimedia Data.- Semantic Modeling for Geographic Information Systems.- Semantic Overlay Networks.- Semantic Social Web.- Semantic Streams.- Semantic Web.- Semantic Web Query Languages.- Semantic Web Services.- Semantics-based Concurrency Control.- Semijoin.- Semijoin Program.- Semi-Structured Data.- Semi-Structured Data Model.- Semi-Structured Database Design.- Semi-Structured Query Languages.- Semi-Supervised Learning.- Sensor Networks.- Sequenced Semantics.- Sequential Patterns.- Serializability.- Serializable Snapshot Isolation.- Service Component Architecture (SCA).- Service Oriented Architecture.- Session.- Shared-Disk Architecture.- Shared-Memory Architecture.- Shared-Nothing Architecture.- Side-Effect-Free View Updates.- Signature Files.- Similarity and Ranking Operations.- Simplicial Complex.- Singular Value Decomposition.- Skyline Queries and Pareto Optimality.- Snapshot Equivalence.- Snapshot Isolation.- Snippet.- Snowflake Schema.- SOAP.- Social Applications.- Social influence.- Social Media Analysis.- Social Media Analytics.- Social Media Harvesting.- Social network analysis.- Social Networks.- Software As-A-Service (SaaS).- Software Transactional Memory.- Software-Defined Storage.- Solid State Drive (SSD).- Sort-Merge Join.- Space-Filling Curves.- Space-Filling Curves for Query Processing.- SPARQL.- Sparse Index.- Spatial and Spatio-Temporal Data Models and Languages.- Spatial and Temporal Data Warehouses .- Spatial Anonymity.- Spatial Data Analysis.- Spatial Data Mining.- Spatial Data Types.- Spatial Datawarehousing.- Spatial Indexing Techniques.- Spatial Join.- Spatial Keyword Search.- Spatial Matching Problems.- Spatial Network Databases.- Spatial Operations and Map Operations.- Spatial Queries in the Cloud.- Spatio-Temporal Data Mining.- Spatio-Temporal Data Types.- Spatio-Temporal Data Warehouses.- Spatiotemporal Interpolation Algorithms.- Spatio-Temporal Selectivity Estimation.- Spatio-Temporal Trajectories.- Specialization and Generalization.- Specificity.- Spectral Clustering.- Split.- Split Transactions.- SQL.- SQL Analytics on Big Data.- SQL Isolation Levels.- SQL-Based Temporal Query Languages.- Stable Distribution.- Stack-based Query Language.- Staged DBMS.- Standard Effectiveness Measures.- Star Index.- Star Schema.- State-based Publish\/Subscribe.- Statistical Data Management.- Statistical Disclosure Limitation For Data Access.- Steganography.- Stemming.- Stop-\u0026amp;-go Operator.- Stoplists.- Storage Access Models.- Storage Area Network.- Storage Consolidation.- Storage Devices.- Storage Grid.- Storage Management.- Storage Management Initiative-Specification.- Storage Manager.- Storage Network Architectures.- Storage Networking Industry Association.- Storage of Large Scale Multidimensional Data.- Storage Power Management.- Storage Protection.- Storage Protocols.- Storage Resource Management.- Storage Security.- Storage Virtualization.- Stored Procedure.- Stream Mining.- Stream Models.- Stream Processing.- Stream processing on modern hardware.- Stream Reasoning.- Stream Sampling.- Stream Similarity Mining.- Streaming Analytics.- Streaming Applications.- Stream-Oriented Query Languages and Operators.- Strong Consistency Models for Replicated Data.- Structural Indexing.- Structure Analytics in Social Media.- Structure Weight.- Structured Data in Peer-to-Peer Systems.- Structured Document Retrieval.- Structured Text Retrieval Models.- Subject Spaces.- Subspace Clustering Techniques.- Success at n.- Succinct Constraints.- Suffix Tree.- Summarizability.- Summarization.- Support Vector Machine.- Supporting Transaction Time Databases.- Symbolic Representation.- Symmetric Encryption.- Synopsis Structure.- Synthetic Microdata.- System R (R*) Optimizer.- Table.- Tabular Data.- Taxonomy: Biomedical Health Informatics.- tBench.- Telic Distinction in Temporal Databases.- Telos.- Temporal Access Control.- Temporal Aggregation.- Temporal Algebras.- Temporal Analytics in Social Media.- Temporal Benchmarks.- Temporal Coalescing.- Temporal Compatibility.- Temporal Conceptual Models.- Temporal Constraints.- Temporal Data Mining.- Temporal Data Models.- Temporal Database.- Temporal Datawarehousing.- Temporal Dependencies.- Temporal Element.- Temporal Expression.- Temporal Generalization.- Temporal Granularity.- Temporal Homogeneity.- Temporal Indeterminacy.- Temporal Integrity Constraints.- Temporal Joins.- Temporal Logic in Database Query Languages.- Temporal Logical Models.- Temporal Object-Oriented Databases.- Temporal Periodicity.- Temporal Projection.- Temporal PSM.- Temporal Query Languages.- Temporal Query Processing.- Temporal Relational Calculus.- Temporal Specialization.- Temporal Strata.- Temporal Support in the SQL Standard.- Temporal Vacuuming.- Temporal Visual Languages.- Temporal XML.- Term Proximity.- Term Statistics for Structured Text Retrieval.- Term Weighting.- Test Collection.- Text Analytics.- Text Analytics in Social Media.- Text Categorization.- Text Clustering.- Text Compression.- Text Generation.- Text Index Compression.- Text Indexing and Retrieval.- Text Indexing Techniques.- Text Mining.- Text Mining of Biological Resources.- Text Representation.- Text Segmentation.- Text Semantic Representation.- Text Stream Processing.- Text Streaming Model.- Text Summarization.- Text Visualization.- TF*IDF.- Thematic Map.- Third Normal Form.- Three-Dimensional GIS and Geological Applications.- Three-Phase Commit.- Tight Coupling.- Time Aggregated Graphs.- Time and Information Retrieval.- Time Domain.- Time in Philosophical Logic.- Time Instant.- Time Interval.- Time Period.- Time Series Query.- Time Span.- Time-Line Clock.- Timeslice Operator.- Topic Detection and Tracking.- Topic Maps.- Topic-based Publish\/Subscribe.- Top-k Queries.- Top-K Selection Queries on Multimedia Datasets.- Topological Data Models.- Topological Relationships.- Trajectory.- Transaction.- Transaction Chopping.- Transaction Management.- Transaction Manager.- Transaction Models - the Read\/Write Approach.- Transaction Time.- Transactional Middleware.- Transactional Processes.- Transactional Stream Processing.- Transaction-Time Indexing.- Tree-based Indexing.- Treemaps.- Triangular Norms.- Triangulated Irregular Network.- Trie.- Trip Planning Queries.- Trust and Reputation in Peer-to-Peer Systems.- Trust in Blogosphere.- Trusted Hardware.- TSQL2.- Tuning Concurrency Control.- Tuple-Generating Dependencies.- Two-Dimensional Shape Retrieval.- Two-Phase Commit.- Two-Phase Commit Protocol.- Two-Phase Locking.- Two-Poisson model.- Type-based Publish\/Subscribe.- U-measure.- Uncertain Data Lineage.- Uncertain Data Mining.- Uncertain Data Models.- Uncertain Data Streams.- Uncertain Data Summarization.- Uncertain Graph Data Management.- Uncertain Spatial Data Management.- Uncertain Top-k Queries.- Uncertainty in Events.- Uncertainty Management in Scientific Database Systems.- Unicode.- Unified Modeling Language.- Union.- Unobservability.- Updates and Transactions in Peer-to-Peer Systems.- Updates through Views.- Usability.- User-Defined Time.- Valid Time.- Valid-Time Indexing.- Value Equivalence.- Variable Time Span.- Vector-Space Model.- Vertically Partitioned Data.- Video.- Video Content Analysis.- Video Content Modeling.- Video Content Structure.- Video Metadata.- Video Querying.- Video Representation.- Video Scene and Event Detection.- Video Segmentation.- Video Sequence Indexing.- Video Shot Detection.- Video Summarization.- View Adaptation.- View Definition.- View Maintenance.- View Maintenance Aspects.- View-based Data Integration.- Views.- Virtual Partitioning.- Visual Analytics.- Visual Association Rules.- Visual Classification.- Visual Clustering.- Visual Content Analysis.- Visual Data Mining.- Visual Formalisms.- Visual Interaction.- Visual Interfaces.- Visual Interfaces for Geographic Data.- Visual interfaces for streaming data.- Visual Metaphor.- Visual On-Line Analytical Processing (OLAP).- Visual Perception.- Visual Query Language.- Visual Representation.- Visualization for Information Retrieval.- Visualization Pipeline.- Visualizing Categorical Data.- Visualizing Clustering Results.- Visualizing Hierarchical Data.- Visualizing Network Data.- Visualizing Quantitative Data.- Volume.- Voronoi Diagrams.- W3C.- WAN Data Replication.- Wavelets on Streams.- Weak Consistency Models for Replicated Data.- Weak Equivalence.- Web 2.0\/3.0.- Web Advertising.- Web Characteristics and Evolution.- Web Crawler Architecture.- Web Data Extraction System.- Web ETL.- Web Harvesting.- Web Information Extraction.- WEB Information Retrieval Models.- Web Mashups.- Web Page Quality Metrics.- Web Question Answering.- Web Search Query Rewriting.- Web Search Relevance Feedback.- Web Search Relevance Ranking.- Web Search Result Caching and Prefetching.- Web Search Result De-duplication and Clustering.- Web Services.- Web Services and the Semantic Web for Life Science Data.- Web Spam Detection.- Web Transactions.- Web Views.- What-If Analysis.- WIMP Interfaces.- Window operator in RDBMS.- Window-based Query Processing.- Windows.- Workflow Constructs.- Workflow Evolution.- Workflow Join.- Workflow Management.- Workflow Management and Workflow Management System.- Workflow Management Coalition.- Workflow Model.- Workflow Model Analysis.- Workflow Patterns.- Workflow Schema.- Workflow Transactions.- Wrapper Induction.- Wrapper Maintenance.- Wrapper Stability.- Write Once Read Many.- XML.- XML Access Control.- XML Attribute.- XML Benchmarks.- XML Compression.- XML Document.- XML Element.- XML Indexing.- XML Information Integration.- XML Integrity Constraints.- XML Metadata Interchange.- XML Metadata Interchange Specification (XMI).- XML Parsing, SAX\/DOM.- XML Process Definition Language.- XML Programming.- XML Publish\/Subscribe.- XML Publishing.- XML Retrieval.- XML Schema.- XML Selectivity Estimation.- XML Storage.- XML Stream Processing.- XML Tree Pattern, XML Twig Query.- XML Tuple Algebra.- XML Typechecking.- XML Types.- XML Updates.- XML Views.- XPath\/XQuery.- XQuery Full-Text.- XQuery Processors.- XSL\/XSLT.- Zero-One Laws.- Zooming Techniques.- α-nDCG.-","brand":"Springer-Verlag New York Inc.","offers":[{"title":"Default Title","offer_id":49408624918871,"sku":"9781461482666","price":999.99,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781461482666.jpg?v=1730503583"},{"product_id":"mastering-snowflake-solutions-9781484280287","title":"Mastering Snowflake Solutions","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003eDesign for large-scale, high-performance queries using Snowflake's query processing engine to empower data consumers with timely, comprehensive, and secure access to data. This book also helps you protect your most valuable data assets using built-in security features such as end-to-end encryption for data at rest and in transit. It demonstrates key features in Snowflake and shows how to exploit those features to deliver a personalized experience to your customers. It also shows how to ingest the high volumes of both structured and unstructured data that are needed for game-changing business intelligence analysis.Mastering Snowflake Solutionsstarts with a refresher on Snowflake's unique architecture before getting into the advanced concepts that make Snowflake the market-leading product it is today. Progressing through each chapter, you will learn how to leverage storage, query processing, cloning, data sharing, and continuous data protection features. This approach allows for greater\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003e1. Snowflake Architecture2. Data Movement3. Cloning4. Managing Security and User Access Control 5. Protecting Data in Snowflake6. Business Continuity and Disaster Recovery7. Data Sharing and the Data Cloud8. Programming9. Advanced Performance Tuning10. Developing Applications in Snowflake","brand":"APress","offers":[{"title":"Default Title","offer_id":49409124270423,"sku":"9781484280287","price":46.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781484280287.jpg?v=1730505526"},{"product_id":"building-the-snowflake-data-cloud-9781484285923","title":"Building the Snowflake Data Cloud","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003eImplement the Snowflake Data Cloud using best practices and reap the benefits of scalability and low-cost from the industry-leading, cloud-based, data warehousing platform. This book provides a detailed how-to explanation, and assumes familiarity with Snowflake core concepts and principles. It is a project-oriented book with a hands-on approach to designing, developing, and implementing your Data Cloud with security at the center. As you work through the examples, you will develop the skill, knowledge, and expertise to expand your capability by incorporating additional Snowflake features, tools, and techniques. Your Snowflake Data Cloud will be fit for purpose, extensible, and at the forefront of both Direct Share, Data Exchange, and Snowflake Marketplace.\u003cdiv\u003e\u003cbr\u003e\u003c\/div\u003e\u003cdiv\u003e\u003cdiv\u003e\n\u003ci\u003e\u003cb\u003eBuilding the Snowflake Data Cloud\u003c\/b\u003e\u003c\/i\u003e helps you transform your organization into monetizing the value locked up within your data. As the digital economy takes hold, with data volume, veloci\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003e\n\u003cb\u003ePart I. Context \u003c\/b\u003e1. The Snowflake Data Cloud 2. Breaking Data Siloes \u003cb\u003ePart II. Concepts \u003c\/b\u003e3. Architecture 4. Account Security5. Role Based Access Control (RBAC)6. Account Usage Store\u003cb\u003ePart III. Tools\u003c\/b\u003e7. Ingesting Data8. Data Pipelines9. Data Presentation10. Semi Structured and Unstructured Data\u003cb\u003ePart IV. Management\u003c\/b\u003e11. Query Optimizer Basics12. Data Management13. Data Modelling14. Snowflake Data Cloud By Example\u003cbr\u003e\n\u003c\/div\u003e\u003c\/div\u003e","brand":"APress","offers":[{"title":"Default Title","offer_id":49409125122391,"sku":"9781484285923","price":999.99,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781484285923.jpg?v=1730505528"},{"product_id":"genetic-algorithms-in-elixir-9781680507942","title":"Genetic Algorithms in Elixir","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eFrom finance to artificial intelligence, genetic algorithms are a powerful tool with a wide array of applications. But you don't need an exotic new language or framework to get started; you can learn about genetic algorithms in a language you're already familiar with. Join us for an in-depth look at the algorithms, techniques, and methods that go into writing a genetic algorithm. From introductory problems to real-world applications, you'll learn the underlying principles of problem solving using genetic algorithms.\u003c\/p\u003e \u003cp\u003eEvolutionary algorithms are a unique and often overlooked subset of machine learning and artificial intelligence. Because of this, most of the available resources are outdated or too academic in nature, and none of them are made with Elixir programmers in mind.\u003c\/p\u003e \u003cp\u003eStart from the ground up with genetic algorithms in a language you are familiar with. Discover the power of genetic algorithms through simple solutions to challenging problems. Use Elixir features to write genetic algorithms that are concise and idiomatic. 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If development was originally intended for the performance of information systems, the issues are now massively moved on logical optimization of the customer relationship, with the main objective to maximize potential sales.\u003c\/p\u003e \u003cp\u003eOn the transdisciplinary approach, engines and recommender systems brings together contributions linking information science and communications, marketing, sociology, mathematics and computing. It deals with the understanding of the underlying models for recommender systems and describes their historical perspective. It also analyzes their development in the content offerings and assesses their impact on user behavior.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003e\u003cp\u003ePREFACE xi\u003cbr\u003e \u003ci\u003eGérald KEMBELLEC, Ghislaine CHARTRON and Imad SALEH\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 1. GENERAL INTRODUCTION TO RECOMMENDER SYSTEMS 1\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eGhislaine CHARTRON and Gérald KEMBELLEC\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1. Putting it into perspective 1\u003c\/p\u003e \u003cp\u003e1.2. An interdisciplinary subject 2\u003c\/p\u003e \u003cp\u003e1.3. The fundamentals of algorithms 4\u003c\/p\u003e \u003cp\u003e1.3.1. Collaborative filtering 4\u003c\/p\u003e \u003cp\u003e1.3.2. Content filtering 7\u003c\/p\u003e \u003cp\u003e1.3.3. Hybrid methods 9\u003c\/p\u003e \u003cp\u003e1.3.4. Conclusion on historical recommendation models 11\u003c\/p\u003e \u003cp\u003e1.4. Content offers and recommender systems 11\u003c\/p\u003e \u003cp\u003e1.4.1. Culture and recommender systems 11\u003c\/p\u003e \u003cp\u003e1.4.2. Recommender systems and the e-commerce of content 16\u003c\/p\u003e \u003cp\u003e1.4.3. The behavior of users 18\u003c\/p\u003e \u003cp\u003e1.5. Current issues 19\u003c\/p\u003e \u003cp\u003e1.6. Bibliography 19\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 2. UNDERSTANDING USERS’ EXPECTATIONS FOR RECOMMENDER SYSTEMS: THE CASE OF SOCIAL MEDIA 25\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eJean-Claude DOMENGET and Alexandre COUTANT\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1. Introduction: the omnipresence of recommender systems 25\u003c\/p\u003e \u003cp\u003e2.2. The social approach to prescription 27\u003c\/p\u003e \u003cp\u003e2.2.1. The theory of the prescription and online interactions 27\u003c\/p\u003e \u003cp\u003e2.2.2. Conditions for recognition of the prescription 29\u003c\/p\u003e \u003cp\u003e2.2.3. The specificities of social media 30\u003c\/p\u003e \u003cp\u003e2.3. Users who do not focus on the prescriptions of platforms 31\u003c\/p\u003e \u003cp\u003e2.3.1. Facebook: the link, the type of activity and the context 32\u003c\/p\u003e \u003cp\u003e2.3.2. Twitter: prescription between peers and explanation of prescription 38\u003c\/p\u003e \u003cp\u003e2.3.3. Conditions for the recognition of a prescription: announcement and enunciation 44\u003c\/p\u003e \u003cp\u003e2.4. A guide for considering recommender systems adapted to different forms of social media 45\u003c\/p\u003e \u003cp\u003e2.5. Conclusion 48\u003c\/p\u003e \u003cp\u003e2.6. Bibliography 49\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 3. RECOMMENDER SYSTEMS AND SOCIAL NETWORKS: WHAT ARE THE IMPLICATIONS FOR DIGITAL MARKETING? 53\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eMaria MERCANTI-GUÉRIN\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1. Social recommendations: an ancient practice revived by the digital age 54\u003c\/p\u003e \u003cp\u003e3.1.1. Recommendations: a difficult management for brands 55\u003c\/p\u003e \u003cp\u003e3.1.2. Internet recommendations: social presence and personalized recommendations 55\u003c\/p\u003e \u003cp\u003e3.2. Social recommendations: how are they used for e-commerce? 58\u003c\/p\u003e \u003cp\u003e3.2.1. Efficiency of recommender systems with regard to the performance of e-commerce websites 58\u003c\/p\u003e \u003cp\u003e3.2.2. Recommender systems used by social networks: from e-commerce to social commerce 59\u003c\/p\u003e \u003cp\u003e3.3. Conclusion 66\u003c\/p\u003e \u003cp\u003e3.4. Bibliography 68\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 4. RECOMMENDER SYSTEMS AND DIVERSITY: TAKING ADVANTAGE OF THE LONG TAIL AND THE DIVERSITY OF RECOMMENDATION LISTS 71\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eMuriel FOULONNEAU, Valentin GROUÈS, Yannick NAUDET and Max CHEVALIER\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1. The stakes associated with diversity within recommender systems 72\u003c\/p\u003e \u003cp\u003e4.1.1. Individual diversity or the individual perception of diversity 73\u003c\/p\u003e \u003cp\u003e4.1.2. The stakes and impacts of aggregate diversity 74\u003c\/p\u003e \u003cp\u003e4.2. Recommendation algorithms and diversity: trends, evaluation and optimization 77\u003c\/p\u003e \u003cp\u003e4.2.1. The tendency for recommendation algorithms to focus on the head 78\u003c\/p\u003e \u003cp\u003e4.2.2. The evaluation of diversity in recommender systems 80\u003c\/p\u003e \u003cp\u003e4.2.3. Recommendation algorithms which favor individual diversity 81\u003c\/p\u003e \u003cp\u003e4.2.4. Recommendation algorithms which favor aggregate diversity 81\u003c\/p\u003e \u003cp\u003e4.2.5. The shift toward user-centered diversity approaches 82\u003c\/p\u003e \u003cp\u003e4.3. Conclusion and new directions 85\u003c\/p\u003e \u003cp\u003e4.4. Bibliography 87\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 5. ISONTRE: INTELLIGENT TRANSFORMER OF SOCIAL NETWORKS INTO A RECOMMENDATION ENGINE ENVIRONMENT 93\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eRana CHAMSI ABU QUBA, Salima HASSAS, Usama FAYYAD, Hammam CHAMSI and Christine GERTOSIO\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1. Summary 93\u003c\/p\u003e \u003cp\u003e5.2. Introduction 94\u003c\/p\u003e \u003cp\u003e5.3. Latest developments, definition and history 97\u003c\/p\u003e \u003cp\u003e5.3.1. Collaborative filtering techniques 97\u003c\/p\u003e \u003cp\u003e5.3.2. General use social networks: what do they contain? 97\u003c\/p\u003e \u003cp\u003e5.3.3. Social recommendation 99\u003c\/p\u003e \u003cp\u003e5.3.4. The recommendation of concepts 100\u003c\/p\u003e \u003cp\u003e5.4. iSoNTRE 101\u003c\/p\u003e \u003cp\u003e5.4.1. iSoNTRE: transformer of social networks 102\u003c\/p\u003e \u003cp\u003e5.4.2. iSoNTRE: the core of recommendation 107\u003c\/p\u003e \u003cp\u003e5.5. Experiments 110\u003c\/p\u003e \u003cp\u003e5.5.1. The preparation of data 110\u003c\/p\u003e \u003cp\u003e5.5.2. Testing methodology 110\u003c\/p\u003e \u003cp\u003e5.5.3. The creation of avatars 111\u003c\/p\u003e \u003cp\u003e5.5.4. Results 112\u003c\/p\u003e \u003cp\u003e5.5.5. Discussion 113\u003c\/p\u003e \u003cp\u003e5.6. Conclusion 114\u003c\/p\u003e \u003cp\u003e5.7. Bibliography 115\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 6. A TWO-LEVEL RECOMMENDATION APPROACH FOR DOCUMENT SEARCH 119\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eManel HMIMIDA and Rushed KANAWATI\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1. Introduction 119\u003c\/p\u003e \u003cp\u003e6.2. Tag recommendation: a brief state of the art 120\u003c\/p\u003e \u003cp\u003e6.3. The hypertagging system 122\u003c\/p\u003e \u003cp\u003e6.3.1. Metadata 122\u003c\/p\u003e \u003cp\u003e6.3.2. Architecture 123\u003c\/p\u003e \u003cp\u003e6.4. Recommendation approach 124\u003c\/p\u003e \u003cp\u003e6.4.1. Presentation 124\u003c\/p\u003e \u003cp\u003e6.4.2. Recommendation algorithm 126\u003c\/p\u003e \u003cp\u003e6.5. Evaluation 127\u003c\/p\u003e \u003cp\u003e6.5.1. Generation of facets 127\u003c\/p\u003e \u003cp\u003e6.5.2. Generation of association rules 129\u003c\/p\u003e \u003cp\u003e6.5.3. Evaluation of recommendation rules 130\u003c\/p\u003e \u003cp\u003e6.6. Conclusion 131\u003c\/p\u003e \u003cp\u003e6.7. Bibliography 132\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 7. COMBINING CONFIGURATION AND RECOMMENDATION TO ENABLE AN INTERACTIVE GUIDANCE OF PRODUCT LINE CONFIGURATION 135\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eRaouia TRIKI , Raúl MAZO and Camille SALINESI\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1. Introduction 135\u003c\/p\u003e \u003cp\u003e7.2. Context 137\u003c\/p\u003e \u003cp\u003e7.2.1. Configuration 137\u003c\/p\u003e \u003cp\u003e7.2.2. Recommendation 139\u003c\/p\u003e \u003cp\u003e7.2.3. Obstacles and challenges of interactive PL configuration 141\u003c\/p\u003e \u003cp\u003e7.3. Overview of the proposed approach 142\u003c\/p\u003e \u003cp\u003e7.4. Preliminary evaluation 148\u003c\/p\u003e \u003cp\u003e7.5. Discussion and related work 148\u003c\/p\u003e \u003cp\u003e7.5.1. Recommendation techniques 148\u003c\/p\u003e \u003cp\u003e7.6. Conclusion and future work 151\u003c\/p\u003e \u003cp\u003e7.7. Bibliography 151\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 8. SEMIO-COGNITIVE SPACES: THE FRONTIER OF RECOMMENDER SYSTEMS 157\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eHakim HACHOUR, Samuel SZONIECKY and Safia ABOUAD\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1. Introduction 157\u003c\/p\u003e \u003cp\u003e8.2. Latest developments: finalized activities, recommender systems and the relevance of information 159\u003c\/p\u003e \u003cp\u003e8.2.1. Cognitive dynamics of finalized activities 159\u003c\/p\u003e \u003cp\u003e8.2.2. The foundations of recommender systems 161\u003c\/p\u003e \u003cp\u003e8.2.3. What information relevance? 166\u003c\/p\u003e \u003cp\u003e8.3. Observable interests for decision theory: a combination of content-based, collaboration based and knowledge-based recommendations 169\u003c\/p\u003e \u003cp\u003e8.3.1. Methodology: meta-analysis and modeling of the process 169\u003c\/p\u003e \u003cp\u003e8.3.2. Analysis and modeling of a macro-process for responding to a call for R\u0026amp;D projects 171\u003c\/p\u003e \u003cp\u003e8.3.3. Analysis and model of a socio-organizational tool for the management of customer complaints 173\u003c\/p\u003e \u003cp\u003e8.4. Discussion and conclusions 177\u003c\/p\u003e \u003cp\u003e8.4.1. Discussion: the performance of the filtering methods and semio-cognitive criteria for relevance 177\u003c\/p\u003e \u003cp\u003e8.5. Conclusions: recommender systems linked to finalized activities 181\u003c\/p\u003e \u003cp\u003e8.5.1. The localization of activities and geographical information systems: a new kind of data 182\u003c\/p\u003e \u003cp\u003e8.5.2. Transparency of the use of personal data, data protection and ownership 183\u003c\/p\u003e \u003cp\u003e8.6. Acknowledgments 185\u003c\/p\u003e \u003cp\u003e8.7. Bibliography 185\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 9. THE FRENCH-SPEAKING LITERARY PRESCRIPTION MARKET IN NETWORKS 191\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eLouis WIART\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1. Introduction 191\u003c\/p\u003e \u003cp\u003e9.2. The economy of prescription 193\u003c\/p\u003e \u003cp\u003e9.2.1. The notion of prescription 193\u003c\/p\u003e \u003cp\u003e9.2.2. From the advisors market to the prescription market 194\u003c\/p\u003e \u003cp\u003e9.3. Methodology 196\u003c\/p\u003e \u003cp\u003e9.4. The competitive structure of the market of online social networks of readers 197\u003c\/p\u003e \u003cp\u003e9.4.1. Pure player networks and the audience strategy 199\u003c\/p\u003e \u003cp\u003e9.4.2. Amateur networks and the survival strategy 201\u003c\/p\u003e \u003cp\u003e9.4.3. Backed networks and the hybridization strategy 202\u003c\/p\u003e \u003cp\u003e9.5. The organization of prescription 204\u003c\/p\u003e \u003cp\u003e9.5.1. Social prescription 205\u003c\/p\u003e \u003cp\u003e9.5.2. Editorial prescription 206\u003c\/p\u003e \u003cp\u003e9.5.3. Algorithmic prescription 207\u003c\/p\u003e \u003cp\u003e9.6. Conclusion: what legitimacy for literary prescription? 208\u003c\/p\u003e \u003cp\u003e9.7. Appendix: list of interviews undertaken 210\u003c\/p\u003e \u003cp\u003e9.8. Bibliography 210\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 10. PRESENTATION OF OFFERED SERVICES: BABELIO, A RECOMMENDATION ENGINE DEDICATED TO BOOKS 213\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eVassil STEFANOV, Guillaume TEISSEIRE and Pierre FRÉMAUX\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1. Introduction 213\u003c\/p\u003e \u003cp\u003e10.2. The problem of qualitative pertinence 216\u003c\/p\u003e \u003cp\u003e10.3. The problem of quantitative pertinence 217\u003c\/p\u003e \u003cp\u003e10.4. Balancing recall and precision 217\u003c\/p\u003e \u003cp\u003e10.5. The issue of sparse data 218\u003c\/p\u003e \u003cp\u003e10.6. Performance and scalability 218\u003c\/p\u003e \u003cp\u003e10.7. A few issues specific to books 219\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 11. PRESENTATION OF THE OFFER OF SERVICES: NOMAO, RECOMMENDER SYSTEMS AND INFORMATION SEARCH 221\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eEstelle DELPECH, Laurent CANDILLIER and Étienne CHAI\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1. Introduction: the actors of Internet recommendation 221\u003c\/p\u003e \u003cp\u003e11.2. Approaches to recommendation 222\u003c\/p\u003e \u003cp\u003e11.3. Nomao: a local outlets search and recommendation engine 223\u003c\/p\u003e \u003cp\u003e11.3.1. Popularity score 223\u003c\/p\u003e \u003cp\u003e11.3.2. Affinity score 224\u003c\/p\u003e \u003cp\u003e11.3.3. Social recommendation 225\u003c\/p\u003e \u003cp\u003e11.4. Prospects: the move toward interactive recommender systems 225\u003c\/p\u003e \u003cp\u003e11.5. Appendix 226\u003c\/p\u003e \u003cp\u003eLIST OF AUTHORS 227\u003c\/p\u003e \u003cp\u003eINDEX 231\u003c\/p\u003e","brand":"ISTE Ltd and John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49413719425367,"sku":"9781848217683","price":125.06,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781848217683.jpg?v=1730521160"},{"product_id":"internet-access-in-vehicular-networks-9783030889906","title":"Internet Access in Vehicular Networks","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003e\u003cp\u003eThis book introduces the Internet access for vehicles as well as novel communication and computing paradigms based on the Internet of vehicles. \u003c\/p\u003e\u003cp\u003eTo enable efficient and reliable Internet connection for mobile vehicle users, this book first introduces analytical modelling methods for the practical vehicle-to-roadside (V2R) Internet access procedure, and employ the interworking of V2R and vehicle-to-vehicle (V2V) to improve the network performance for a variety of automotive applications. \u003c\/p\u003e\u003cp\u003eIn addition, the wireless link performance between a vehicle and an Internet access station is investigated, and a machine learning based algorithm is proposed to improve the link throughout by selecting an efficient modulation and coding scheme.\u003c\/p\u003e\u003cp\u003eThis book also investigates the distributed machine learning algorithms over the Internet access of vehicles. A novel broadcasting scheme is designed to intelligently adjust the training users that are involved in the iteration rounds for an asynchronous federated learning scheme, which is shown to greatly improve the training efficiency. This book conducts the fully asynchronous machine learning evaluations among vehicle users that can utilize the opportunistic V2R communication to train machine learning models. \u003c\/p\u003e\u003cp\u003eResearchers and advanced-level students who focus on vehicular networks, industrial entities for internet of vehicles providers, government agencies target on transportation system and road management will find this book useful as reference. Network device manufacturers and network operators will also want to purchase this book. \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003e\u003cp\u003eOverview of Internet Access of  Vehicular Networks.- Internet Access Modeling of  Vehicular Internet Access.- V2X Interworking via Vehicular Internet Access.- Intelligent Link Management for Vehicular Internet Access.- Intelligent Networking enabled Vehicular Distributed Learning.- Conclusion and Future Works.\u003c\/p\u003e","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":49415655752023,"sku":"9783030889906","price":999.99,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030889906.jpg?v=1730527675"},{"product_id":"harnessing-the-power-of-analytics-9783030897116","title":"Harnessing the Power of Analytics","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003eThis text highlights the difference between analytics and data science, using predictive analytic techniques to analyze different historical data, including aviation data and concrete data, interpreting the predictive models, and highlighting the steps to deploy the models and the steps ahead. The book combines the conceptual perspective and a hands-on approach to predictive analytics using SAS VIYA, an analytic and data management platform. The authors use SAS VIYA to focus on analytics to solve problems, highlight how analytics is applied in the airline and business environment, and compare several different modeling techniques.  They decipher complex algorithms to demonstrate how they can be applied and explained within improving decisions.\u003cbr\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003eChapter 1. Introduction to Analytics and Data Science.\u003cp\u003e\u003c\/p\u003e  \u003cp\u003eChapter 2. Data Types Structure \u0026amp; Data Preparation Process.\u003c\/p\u003e  \u003cp\u003eChapter 3. Data Exploration and Data Visualization.\u003c\/p\u003e  \u003cp\u003eChapter 4. Evaluating Predictive Performance.\u003c\/p\u003e  \u003cp\u003eChapter 5. Decision Trees \u0026amp; Ensemble.\u003c\/p\u003e  \u003cp\u003eChapter 6. Regression Models.\u003c\/p\u003e  \u003cp\u003eChapter 7. Neural Networks.\u003c\/p\u003e  \u003cp\u003eChapter 8. Model Deployment.\u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":49415656571223,"sku":"9783030897116","price":999.99,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030897116.jpg?v=1730527677"},{"product_id":"advanced-analytics-and-learning-on-temporal-data-6th-ecml-pkdd-workshop-aaltd-2021-bilbao-spain-september-13-2021-revised-selected-papers-9783030914448","title":"Advanced Analytics and Learning on Temporal Data:","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003e\u003cp\u003eThis book constitutes the refereed proceedings of the 6th ECML PKDD Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2021, held during September 13-17, 2021. The workshop was planned to take place in Bilbao, Spain, but was held virtually due to the COVID-19 pandemic. \u003c\/p\u003e  The 12 full papers presented in this book were carefully reviewed and selected from 21 submissions. They focus on the following topics: Temporal Data Clustering; Classification of Univariate and Multivariate Time Series; Multivariate Time Series Co-clustering; Efficient Event Detection; Modeling Temporal Dependencies; Advanced Forecasting and Prediction Models; Cluster-based Forecasting; Explanation Methods for Time Series Classification; Multimodal Meta-Learning for Time Series Regression; and Multivariate Time Series Anomaly Detection.\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e\u003cbr\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003e\u003cp\u003eOral Presentation.- Ranking by Aggregating Referees: Evaluating the Informativeness of Explanation Methods for Time Series Classification.- State Space approximation of Gaussian Processes for time-series forecasting.- Fast Channel Selection for Scalable Multivariate Time Series Classification.- Temporal phenotyping for characterisation of hospital care pathways of COVID patients.- A New Multivariate Time Series Co-clustering Non-Parametric Model Applied to Driving-Assistance Systems Validation.- TRAMESINO: Trainable Memory System for Intelligent Optimization of Road Traffic Control.- Detection of critical events in renewable energy production time series.- Poster Presentation.- Multimodal Meta-Learning for Time Series Regression.- Cluster-based Forecasting for Intermittent and Non-intermittent Time Series.- State discovery and prediction from multivariate sensor data.- RevDet: Robust and Memory Efficient Event Detection and Tracking in Large News Feeds.- From Univariate to Multivariate Time Series Anomaly Detection with Non-Local Information.\u003c\/p\u003e\u003cbr\u003e\u003cp\u003e\u003c\/p\u003e","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":49415658832215,"sku":"9783030914448","price":999.99,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030914448.jpg?v=1730527685"}],"url":"https:\/\/bookcurl.com\/collections\/expert-systems-knowledge-based-systems.oembed?page=12","provider":"Book Curl","version":"1.0","type":"link"}