Databases / Data management Books

Book SynopsisCustomer Relationship Management in the Digital Age charts the concepts, strategies, benefits, and technologies of CRM in an evolving and increasingly digital business landscape.It empowers readers with the skills to use CRM to forge enduring customer connections, optimize experiences, and drive loyalty across diverse industries and markets. Building upon existing literature, this guide offers a holistic approach that bridges theory and practice, making complex CRM concepts accessible to a wide audience. It integrates the latest technological advances, market trends, and customer centric initiatives, providing a comprehensive view of CRM's role in an increasingly customer-driven era. Pedagogical features include case studies, practical strategies and real-world examples, as well as chapter summaries and discussion questions to guide the reader through the key learning points of each chapter.This helpful book enables readers to navigate the complexities of CRM im

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Book SynopsisData Analytics Applied to the Mining Industry describes the key challenges facing the mining sector as it transforms into a digital industry able to fully exploit process automation, remote operation centers, autonomous equipment and the opportunities offered by the industrial internet of things. It provides guidelines on how data needs to be collected, stored and managed to enable the different advanced data analytics methods to be applied effectively in practice, through use of case studies, and worked examples. Aimed at graduate students, researchers, and professionals in the industry of mining engineering, this book: Explains how to implement advanced data analytics through case studies and examples in mining engineering Provides approaches and methods to improve data-driven decision making Explains a concise overview of the state of the art for Mining Executives and Managers Highlights and describes cTable of Contents1. Digital Transformation of Mining. 2. Data Analytics and the Mining Value Chain. 3. Data Collection, Storage and Retrieval. 4. Making Sense of Data. 5. Analytics Toolset. 6. Making Decisions based on Analytics. 7. Process Performance Analytics. 8. Process Maintenance Analytics. 9. Data Analytics for Energy Efficiency and Gas Emission Reduction. 10. Future Skills Requirements.

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Book SynopsisDavid Kroenke has many years of teaching experience at Colorado State University, Seattle University, and the University of Washington. He has led dozens of seminars for college professors on the teaching of information systems and technology; in 1991, the International Association of Information Systems named him Computer Educator of the Year. In 2009, David was named Educator of the Year by the Association of Information Technology Professionals-Education Special Interest Group (AITP-EDSIG). David worked for the US Air Force and Boeing Computer Services. He was a principal in the startup of three companies, serving as the vice president of product marketing and development for the Microrim Corporation and as chief of database technologies for Wall Data, Inc. He is the father of the semantic object data model. David's consulting clients have included IBM, Microsoft, and Computer Sciences Corporations, as well as numerous smaller companies. Recently, David hTable of ContentsPart I: Getting Started 1. Introduction 2. Introduction to Structured Query Language Part II: Database Design 3.The Relational Model and Normalization 4. Database Design Using Normalization 5. Data Modeling and the Entity-Relationship Model 6. Transforming Data Models in Database Designs Part III: Database Implementation 7.SQL for Database Construction and Application Processing 8. Database Redesign Part IV: Multiuser Database Processing 9. Managing Multiuser Databases 10.Managing Databases with SQL Server 2014, Oracle Database 12c, and MySQL 5.7 Online Chapter: 10A.Managing Databases with SQL Server 2014 Online Chapter: 10B.Managing Databases with Oracle 12c Online Chapter: 10C.Managing Databases with MySQL 5.7 Part V: Database Access Standards 11. The Web Server Environment 12. Big Data, Data Warehouses, and Business Intelligence Systems Online Appendix A. Getting Started with Microsoft Access 2013 Online Appendix B. Getting Started in Systems Analysis and Design Online Appendix C. E-R Diagrams and the IDEF1X Standard Online Appendix D. E-R Diagrams and the UML Standard Online Appendix E. Getting Started with MySQL Workbench Data Modeling Tools Online Appendix F. Getting Started with Microsoft Vision 2013 Online Appendix G. Data Structures for Database Processing Online Appendix H. The Semantic Object Model Online Appendix I. Getting Started with Web Servers, PHP and the Eclipse PDT Online Appendix J. Business Intelligence Systems Online Appendix K. Big Data

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Book SynopsisEntity Framework 6 Recipes provides an exhaustive collection of ready-to-use code solutions for Entity Framework, Microsoft's model-centric, data-access platform for the .NET Framework and ASP.NET development.Table of Contents Getting Started with Entity Framework Entity Data Modeling Fundamentals Querying an Entity Data Model Using Entity Framework in ASP.NET Loading Entities and Navigation Properties Beyond the Basics with Modeling and Inheritance Working with Object Services Plain Old CLR Objects Using the Entity Framework in N-Tier Applications Stored Procedures Functions Customizing Entity Framework Objects Improving Performance Concurrency

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Book SynopsisWhether you're new to Oracle or an old hand who has yet to test the waters of APEX, Beginning Oracle Application Express 5 introduces the processes and best practices you'll need to become proficient with APEX.Table of Contents1. An Introduction to APEX 2. A Developer's Overview3. Identifying the Problem and Designing the Solution4. SQL Workshop5. Application and Navigational Components6. Forms & Reports—The Basics7. Forms & Reports—Advanced8. Programmatic Elements9. Security10. Application Deployment11. Understanding Websheets12. A WebSheets Example13. Extended Developer Tools14. Managing Workspaces15. Team Development16. Dynamic Actions17. Appendix A

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Book Synopsis Gain insight into essential data science skills in a holistic manner using data engineering and associated scalable computational methods. This book covers the most popular Python 3 frameworks for both local and distributed (in premise and cloud based) processing. Along the way, you will be introduced to many popular open-source frameworks, like, SciPy, scikitlearn, Numba, Apache Spark, etc. The book is structured around examples, so you will grasp core concepts via case studies and Python 3 code. As data science projects gets continuously larger and more complex, software engineering knowledge and experience is crucial to produce evolvable solutions. You''ll see how to create maintainable software for data science and how to document data engineering practices. This book is a good starting point for people who want to gain practical skills to perform data science. All the code willTable of ContentsChapter 1. Introduction to Data ScienceNo of pages: 10This chapter introduces the reader to data science, and describes the major stages of working with data (collect, explore, preprocess, visualize, predict, and infer knowledge). It sets the common expectations what constitutes a data science domain. This chapter will elaborate about Anaconda IDE, which will be used in the book.Chapter 2. Data AcquisitionNo of pages: 40This chapter will introduce a reader how to retrieve and store data from/to various data sources: text files (including various formats like CSV, XML and JSON), binary files (including Apache Avro), Web accessible data, relational databases, NoSQL databases, Apache Arrow (as efficient and novel columnar data storage system), multi-modal databases, and network databases. This chapter will also introduce BeautifulSoup to work with XML and HTML.Chapter 3. Basic Data ProcessingNo of pages: 40These are standard Python libraries for scientific computing and processing data. NumPy encompasses all sorts of data structures required during data analysis. Here, we will provide examples that will illuminate the importance of sophisticated frameworks, and reuse based software engineering in the realm of data science.Chapter 4. Documenting WorkNo of pages: 20This chapter introduces the most popular computing environment for data analysis. It makes sharing of results between data scientist possible in an easily reproducible manner.Chapter 5. Transformation and Packaging of DataNo of pages: 30This chapter illuminates a critical data science framework that is built upon NumPy. It provides excellent data structures for handling data frames and series.Chapter 6. VisualizationNo of pages: 40This chapter introduces various ways to visualize data; summary statistics or tabular representations are of limited value in exploring data. The following frameworks will the topic of this chapter: matplotlib, glueviz, Bokeh, and orange3. Visualization is important both while doing exploratory analysis as well as when generating effective reports.Chapter 7. Prediction and InferenceNo of pages: 50This chapter will talk about all techniques and technologies to properly scale data science efforts. It will teach readers how to create systems, that may formulate answers on unseen data, or find hidden patterns in data. It will elaborate about supervised, unsupervised, deep, and reinforcement learning methods. Moreover, it will introduce Apache Spark with MLib (both in batch and stream modes) as well as TensorFlow. The following frameworks will also be the topic of this chapter: XGBoost, sci-kit learn and Keras with PyTorch.Chapter 8. Network AnalysisNo of pages: 40This chapter explores the ways to analyze complex networks and graphs. This chapter will introduce Apache Spark GraphX, Apache Giraph, and NetworkX. This chapter will also introduce spectral graph analysis, which is an interesting approximate, non-linear, and non-parametric machine learning method.Chapter 9. Data Science Process EngineeringNo of pages: 20This chapter will elaborate how to share and customize data science practices/methods used by teams via OMG Essence.Chapter 10. Multi-agent Systems, Game Theory and Machine LearningNumber of pages: 30This chapter explores advanced data-oriented applications, where data are produced and consumed by self-governed intelligent agents. The chapter introduces the reader to the concept of multi-agent systems, game theoretic methods and models as well as associated learning algorithms.Chapter 11. Probabilistic Graphical ModelsNumber of pages: 30This chapter explains the most sophisticated form of a graph structure to model many advanced data science problems. Nodes in the graph denote random variables, while the links represent relations between those variables. This chapter equips the reader with a method that may be used when simpler solutions aren’t satisfactory.Chapter 12. Security in Data ScienceNumber of pages: 20This chapter presents techniques to anonymize data, and to deal with situations when learning methods must cope with adversarial modifications (a.k.a. adversarial machine learning). This chapter also talks about ways to protect data both in transit and in rest.Appendix A - Crash Course in Python 3No of pages: 20This chapter will briefly teach readers about Python 3, and explain why Python 3 is a perfect choice for doing data science.

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Book SynopsisPart 1: Basics.- Chapter 1: Data Science in Action.- Chapter 2: Getting Started.- Chapter 3: Data Visualization.- Part 2: More Programming.- Chapter 4: Statistical Analysis and Linear Models.- Chapter 5: Advanced Data Preprocessing.- Chapter 6: Preparing Data for Analysis.- Part 3: Advanced Topics.- Chapter 7: Regression.- Chapter 8: SAS Visual Statistics Viya.- Chapter 9: What Is Next?.- Appendix: Resources.- Table of Contents

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Book Synopsis Access much-needed information for building scalable, high-concurrency applications and deploying them against the Oracle Database. This new edition is updated to be current with Oracle Database 19. It includes a new chapter with troubleshooting recipes to help you quickly diagnose and resolve locking problems that are urgent and block production. Good transaction design is an important facet of highly-concurrent applications that are run by hundreds, even thousands, of users who are executing transactions at the same time. Transaction design, in turn, relies on a good understanding of how the database engine manages the locking of resources to prevent access conflicts and data loss that might otherwise result from concurrent access to data in the database. This book provides a solid and accurate explanation of how locking and concurrency are dealt with by Oracle Database. You will learn how the Oracle Database architecture accommodates user transactiTable of Contents1. Getting Started 2. Locking and Blocking 3. Locks, Latches, and Mutexes 4. Concurrency and Multiversioning 5. Transactions 6. Redo and Undo 7. Investigating Redo 8. Investigating Undo 9. Troubleshooting

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Book SynopsisUnderstand the essentials of the Snowflake Database and the overall Snowflake Data Cloud. This book covers how Snowflake's architecture is different from prior on-premises and cloud databases. The authors also discuss, from an insider perspective, how Snowflake grew so fast to become the largest software IPO of all time. Snowflake was the first database made specifically to be optimized with a cloud architecture. This book helps you get started using Snowflake by first understanding its architecture and what separates it from other database platforms you may have used. You will learn about setting up users and accounts, and then creating database objects. You will know how to load data into Snowflake and query and analyze that data, including unstructured data such as data in XML and JSON formats. You will also learn about Snowflake's compute platform and the different data sharing options that are available.What YouWill LearnRun analytics in the Snowflake Data CloudCreate users and Table of Contents1. The Snowflake Data Cloud2. Snowflake Quick Start3. Snowflake Data Cloud Architecture4. Snowflake Web Interface -- Classic Console5. Snowflake Web Interface -- Preview App (Snowsight)6. Account Management7. Security8. Database Objects9. Querying and Cloning Data in Snowflake10. How Snowflake Compute Works11. Semi-Structured Data in Snowflake12. Loading Data13. Unloading Data14. Data Sharing, Data Exchanges, and the Snowflake Data Marketplace

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Book SynopsisUse this one-stop resource for both basic and advanced administration of Exchange Server 2019 and 2016. It will help you in running an Exchange environment, whether it be 100% on-premises or a hybrid configuration with Exchange Online (as part of Office 365). This revised version is divided into four parts, describing Exchange infrastructure, upgrading Exchange server, integration with Office 365, and security and compliance. In the first part, you will go through a short introduction of Exchange server followed by its installation and configuration. You will learn client access along with Exchange mailbox and managing Exchange recipients. In the second part, you will learn how to upgrade from Exchange 2010 to 2016 and from 2013 to Exchange 2019. The third part is dedicated to the Exchange integration with Office 365, followed by the last part that teaches you how to secure your Exchange environment and its compliance. After reading this book, you will understand best practices, doTable of ContentsSection 1 - Exchange Infrastructure 1. Introduction 1.1. History of Exchange server 1.2. Exchange 2016 or Exchange 2019? 1.3. Exchange Admin Center 1.4. PowerShell 1.5. Exchange and Active Directory 2. Installing and configuring Exchange 2.1. Designing the Exchange environment 2.2. Testing the Exchange environment 2.3. Building the Exchange environment 2.4. Exchange Edge Transport Server 2.5. Cumulative Updates 3. Exchange and Client Access 3.1. Clients 3.2. Outlook on the Web 3.3. Outlook 3.4. Autodiscover 3.5. Exchange Web Services 3.6. ActiveSync 3.7. Namespaces 3.8. Virtual Directories 3.9. Certificates 3.10. Client Access High Availability 4. Exchange Mailbox and Mailbox server 4.1. Mailbox databases 4.2. Backup and Restore 4.3. High Availability 4.4. Mailbox database High Availability 5. Managing Exchange Recipients 5.1. Managing Recipients 5.2. Mailboxes 5.3. Distribution Groups 5.4. Contacts 5.5. Public Folders 5.6. Address Lists 5.7. Global Address List 5.8. Custom Address List 5.9. Offline Address Books 5.10. Address Book Policies 6. Exchange Transport 6.1. Transport pipeline 6.2. Send and Receive Connectors 6.3. SMTP Relay 6.4. Edge Transport server 6.5. Load balancing transport Section 2 - Upgrading Exchange server 7. Upgrading from Exchange 2010 to Exchange 2016 7.1. Preparing Active Directory 7.2. Installing the Exchange servers 7.3. Change client access 7.4. Move Mailboxes 7.5. Move Public Folders 7.6. Decommission Exchange 2010 8. Upgrading from Exchange 2013 to Exchange 2019 8.1. Preparing AD en Installing Exchange is identical to 7.1 and 7.2 8.2. Client access in 2013/2019 coexistence 8.3. Move Mailboxes and Public Folders 8.4. Decommission Exchange 2013 Section 3 - integration with Office 365 9. Exchange Hybrid 9.1. Identities and source of authority 9.2. Managing Hybrid Recipients 9.3. Federation 9.4. Federation with other organizations 9.5. Organizational relationships 9.6. Sharing policies 9.7. Directory Synchronization 9.8. Exchange Hybrid Configuration Wizard 9.9. Autodiscover continued 9.10. Mailflow in coexistence 9.11. Exchange Online Protection 9.12. Modern Authentication 9.13. Bulk Email Section 4 - Security and Compliance 10. Securing your Exchange environment 10.1. Hybrid Modern Authentication 10.2. Message hygiene 10.3. Multi Factor Authentication 10.4. Role Based Access Control 11. Compliance 11.1. Archiving 11.2. Journaling 11.3. In-place hold 11.4. In-place eDiscovery 11.5. Messaging Records management 11.6. Data Loss Prevention 11.7. Auditing 11.8. Reporting

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Book SynopsisLeverage the power of Artificial Intelligence (AI) to drive the growth and success of your organization. This book thoroughly explores the reasons why it is so hard to implement AI, and highlights the need to reconcile the motivations and goals of two very different groups of people,business-minded and technical-minded.Divided into four main parts (First Principles,The Why,The What,The How), you'll review case studies and examples from companies that have successfully implemented AI. Part 1 provides a comprehensive overview of the First Principles approach and its basic conventions. Part 2 provides an in-depth look at the current state of AI and why it is increasingly important to businesses of all sizes. Part 3 delves into the key concepts and technologies of AI. Part 4 shares practical guidance and actionable steps for businesses looking to implement AI.Grow Your Business with AI is a must-read for anyone looking to understand and harness the power of AI for business growth and to stTable of ContentsPart I. Introduction and First Principles.- 1. Defining the Problem and Opportunity.- 2. First Principles Methodology.- 3. First Principles for the Key Areas Needed for AI.- 4. The Barriers for Implementing AI.- Part II. The WHY- 5. Introduction to AI and its Role in Business.- 6. Key Trends in AI.- 7. Data Monetization with AI.- Part III.The WHAT.- 8. Overview of AI Concepts and Technologies.- 9. Supervised and Unsupervised Learning.- 10. Neural Networks, Deep Learning and Foundational Models.- 11. Creating an AI Roadmap fully aligned to Enterprise Strategies.- 12. Funding and Measuring the AI Journey.- 13. How to Approach Open Data.- Part IV. The HOW.- 14. Organization & Governance.- 15. Identifying Use Cases and Assembling a Team.- 16. Selecting Tools and Platforms.- 17. Architecting AI Apps: When/How to be Cloud Native.- 18. Integrating AI into Existing Systems and Processes.- 19. Case Studies and Examples.- 20. Responsible AI: Understanding the Ethical and Regulatory Implications of AI.- 21. Scaling AI in the Enterprise: AI Maturity Model.- Part V. The Future.- 22. How Younger Generations See the Future.- 23. AI Future Trends and It's Considerations for Business.- 24. Conclusion.- Appendix.

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Book SynopsisWork with petabyte-scale datasets while building a collaborative, agile workplace in the process. This practical book is the canonical reference to Google BigQuery, the query engine that lets you conduct interactive analysis of large datasets.

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Book SynopsisThis hands-on guide shows you how to get started with Cloud Run, a container-based serverless product on Google Cloud. Through the course of this book, you'll learn how to deploy several example applications that highlight different parts of the serverless stack on Google Cloud.

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Book SynopsisWith this hands-on guide, anyone looking for an introduction to Spark will learn practical algorithms and examples using PySpark. In each chapter, author Mahmoud Parsian shows you how to solve a data problem with a set of Spark transformations and algorithms.

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Book SynopsisWith this hands-on guide, intermediate Excel users will gain a solid understanding of analytics and the data stack. By the time you complete this book, you'll be able to conduct exploratory data analysis and hypothesis testing using a programming language.

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Book SynopsisNew to SQLScript - or maybe looking to brush up on a specific task? Whatever your skill level, this comprehensive guide to SQLScript for SAP HANA is for you! Master language elements, data types, and the function library. Learn to implement SAP HANA database procedures and functions using imperative and declarative SQLScript.

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Book SynopsisData is everywhere - it's just not very well connected, which makes it super hard to relate dataset to dataset. Using graphs as the underlying glue, you can readily join data together and create navigation paths across diverse sets of data. Add Elixir, with its awesome power of concurrency, and you'll soon be mastering data networks. Learn how different graph models can be accessed and used from within Elixir and how you can build a robust semantics overlay on top of graph data structures. We'll start from the basics and examine the main graph paradigms. Get ready to embrace the world of connected data! Graphs provide an intuitive and highly flexible means for organizing and querying huge amounts of loosely coupled data items. These data networks, or graphs in math speak, are typically stored and queried using graph databases. Elixir, with its noted support for fault tolerance and concurrency, stands out as a language eminently suited to processing sparsely connected and distributed datasets. Using Elixir and graph-aware packages in the Elixir ecosystem, you'll easily be able to fit your data to graphs and networks, and gain new information insights. Build a testbed app for comparing native graph data with external graph databases. Develop a set of applications under a single umbrella app to drill down into graph structures. Build graph models in Elixir, and query graph databases of various stripes - using Cypher and Gremlin with property graphs and SPARQL with RDF graphs. Transform data from one graph modeling regime to another. Understand why property graphs are especially good at graph traversal problems, while RDF graphs shine at integrating different semantic models and can scale up to web proportions. Harness the outstanding power of concurrent processing in Elixir to work with distributed graph datasets and manage data at scale. What You Need: To follow along with the book, you should have Elixir 1.10+ installed. The book will guide you through setting up an umbrella application for a graph testbed using a variety of graph databases for which Java SDK 8+ is generally required. Instructions for installing the graph databases are given in an appendix.

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Book SynopsisData is a valuable corporate asset and its effective management is vital to an organisation’s success and survival. With this book you will learn to master the key principles of data management and use them to implement best practices in your organization. This professional guide covers all the key areas of data management, including database development and corporate data modelling. It is business-focused, providing the knowledge and techniques required to successfully implement the data management function. This fully updated new edition provides new chapters on the most important data topics such as big data, artificial intelligence, linked data and concept systems. Principles of Data Management is fully aligned with syllabus for the BCS Professional Certificate in Data Management Essentials, making this the go-to text to unlocking the value of your data. Ideal for business managers and all involved in the development of information systems as well as data management professionals Comprehensive and descriptive view of data management Suitable for all levels, from beginners to advanced learners Must-read for anyone involved in the development of systems to manage data Trade ReviewThis book is an excellent guide to understanding data management theory and techniques. It works at all levels: from beginner to advanced, and from reference source to the practicalities of implementation. I would highly recommend to anyone wanting to get to grips with data management, regardless of experience in the field. -- Ian Wallis, Managing Director, Data Strategists LtdKeith has developed a broad and thorough understanding of all aspects of data management over many years, so is without doubt one of the authorities on data management. This updated book includes reference to a number of new techniques as well as refining existing guidance on data modelling and database structures. Keith clearly explains both the importance of planning and analysis of databases and repositories and an explanation of key techniques to achieve this. A ‘must buy’ for the bookshelf of any data management practitioner. -- Julian Schwarzenbach, Chair of the BCS Data Management Specialist GroupThis book provides a comprehensive and descriptive view of data management within a database setting. This is a must read for anyone involved in the development of systems to manage data. This book is as useful as it is interesting. It covers everything you need to know about getting the most out of your data management processes and architecture. -- Ian Rush, Data & Process Advantage LtdTable of ContentsPart 1: Preliminaries Chapter 1 Data and the enterprise Chapter 2 Databases and their development Chapter 3 What is data management? Part 2: Data Administration Chapter 4 Corporate data modelling Chapter 5 Data definition and naming Chapter 6 Metadata Chapter 7 Data quality Chapter 8 Data accessibility Chapter 9 Master data management Part 3: Database and Repository Administration Chapter 10 Database administration Chapter 11 Repository administration Part 4: The Data Management Environment Chapter 12 The use of packaged application software Chapter 13 Distributed data and databases Chapter 14 Business intelligence Chapter 15 Object orientation Chapter 16 Multimedia Chapter 17 Integrating data and web technology Chapter 18 Linked data Chapter 19 Concept systems Chapter 20 Big data and artificial intelligence Appendices Appendix A Comparison of data modelling notations Appendix B Generic data models Appendix C HTML and XML Appendix D Techniques and skills for data management Appendix E Data strategy Appendix F International standards for data management Appendix G The BCS Data Management Essentials syllabus

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a huge range and FREE tracked UK delivery on ALL orders.

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Book SynopsisThis book constitutes the workshop proceedings of the 24th International Conference on Database Systems for Advanced Applications, DASFAA 2019, held in Chiang Mai, Thailand, in April 2019. The 14 full papers presented were carefully selected and reviewed from 26 submissions to the three following workshops: the 6th International Workshop on Big Data Management and Service, BDMS 2019; the 4th International Workshop on Big Data Quality Management, BDQM 2019; and the Third International Workshop on Graph Data Management and Analysis, GDMA 2019. This volume also includes the short papers, demo papers, and tutorial papers of the main conference DASFAA 2019.Table of ContentsThe 6th International Workshop on Big Data Management and Service (BDSM 2019).- A Probabilistic Approach for Inferring Latent Entity Associations in Textual Web Contents.- UHRP Uncertainty-Based Pruning Method for Anonymized Data Linear Regression.- Meta-path based MiRNA-disease Association Prediction.- Medical Question Retrieval based on Siamese Neural Network and Transfer learning method.- An adaptive Kalman filter based Ocean Wave Prediction Model using Motion Reference Unit Data.- ASLM: Adaptive Single Layer Model for Learned Index.- SparseMAAC: Sparse Attention for Multi-Agent Reinforcement Learning.- The 4th International Workshop on Big Data Quality Management (BDQM 2019).- Identifying Reference Relationship of Desktop Files Based on Access Logs.- Visualization of Photo Album: Selecting a Representative Photo of a Specific Event.- Data Quality Management in Institutional Research Output Data Center.- Generalized Bayesian Structure Learning from Noisy Datasets.- The Third International Workshop on Graph Data Management and Analysis (GDMA 2019).- ANDMC: An Algorithm for Author Name Disambiguation Based on Molecular Cross Clustering.- Graph Based Aspect Extraction and Rating Classification of Customer Review Data.- Streaming Massive Electric Power Data Analysis Based on Spark Streaming.- Short Papers.- Deletion Robust k-Coverage Queries.- Episodic Memory Network with Self-Attention for Emotion Detection.- Detecting Suicidal Ideation with Data Protection in Online Communities.- Hierarchical Conceptual Labeling.- Anomaly Detection in Time-Evolving Attributed Networks.- A Multi-task Learning Framework for Automatic Early Detection of Alzheimer’s.- Top-k Spatial Keyword Query with Typicality and Semantics.- Align Reviews with Topics in Attention Network for Rating Prediction.- PSMSP: A Parallelized Sampling-based Approach for Mining Top-k Sequential Patterns in Database Graphs.- Value-Oriented Ranking of Online Reviews Based on Reviewer-influenced Graph.- Ancient Chinese Landscape Painting Composition Classification by Using Semantic Variational Autoencoder.- Learning Time-Aware Distributed Representations of Locations from Spatio-Temporal Trajectories.- Hyper2vec: Biased Random Walk for Hyper-Network Embedding.- Privacy-preserving and dynamic spatial range aggregation query processing in wireless sensor networks.- Adversarial Discriminative Denoising for Distant Supervision Relation Extraction.- Nonnegative Spectral Clustering for Large-Scale Semi-Supervised Learning.- Distributed PARAFAC Decomposition Method based on In-Memory Big Data System.- GPU-Accelerated Dynamic Graph Coloring.- Relevance-based Entity Embedding.- An Iterative Map-Trajectory Co-Optimisation Framework Based on Map-Matching and Map Update.- Exploring Regularity in Traditional Chinese Medicine Clinical Data Using Heterogeneous Weighted Networks Embedding.- AGREE: Attentive Tour Group Recommendation with Multi-Modal Data.- Random Decision DAG: An Entropy Based Compression Approach for Random Forest.- Generating Behavior Features for Cold-Start Spam Review Detection.- TCL: Tensor-CNN-LSTM for Travel Time Prediction with Sparse Trajectory Data.- A Semi-supervised Classification Approach for Multiple Time-varying Networks with Total Variation.- Multidimensional Skylines Over Streaming Data.- A domain adaptation approach for multistream classification.- Gradient Boosting Censored Regression for Winning Price Prediction in Real-Time Bidding.- Deep Sequential Multi-task Modeling for Next Check-in Time and Location Prediction.- SemiSync: Semi-supervised Clustering by Synchronization.- Neural Review Rating Prediction with Hierarchical Attentions and Latent Factors.- MVS-match: An Efficient Subsequence Matching Approach Based on the Series Synopsis.- Temporal-Spatial Recommendation for On-demand Cinemas.- Finding the key influences on the house price by Finite Mixture Model based on the real estate data in Changchun.- Semi-supervised Clustering with Deep Metric Learning.- Spatial Bottleneck Minimum Task Assignment with Time-delay.- A Mimic Learning Method for Disease Risk Prediction with Incomplete Initial Data.- Hospitalization Behavior Prediction Based on Attention and Time Adjustment Factors in Bidirectional LSTM.- Modeling Item Category for Effective Recommendation.- Distributed Reachability Queries on Massive Graphs.- Edge-Based Shortest Path Caching in Road Networks.- Extracting Definitions and Hypernyms with a Two-Phase Framework.- Tag Recommendation by Word-Level Tag Sequence Modeling.- A New Statistics Collecting Method with Adaptive Strategy.- Word Sense Disambiguation with Massive Contextual Texts.- Learning DMEs from Positive and Negative Examples.- Serial and Parallel Recurrent Convolutional Neural Networks for Biomedical Named Entity Recognition.- DRGAN: A GAN-based Framework for Doctor Recommendation in Chinese On-line QA Communities.- Attention-based Abnormal-Aware Fusion Network for Radiology Report Generation.- LearningTour: A Machine Learning Approach for Tour Recommendation based on Users’ Historical Travel Experience.- TF-Miner: Topic-specific Facet Mining by Label Propagation.- Fast Raft Replication for Transactional Database Systems over Unreliable Networks.- Parallelizing Big De Bruijn Graph Traversal for Genome Assembly on GPU Clusters.- GScan: Exploiting Sequential Scans for Subgraph Matching.- SIMD Accelerates the Probe Phase of Star Joins in Main Memory Databases.- A Deep Recommendation Model Incorporating Adaptive Knowledge-based Representations.- BLOMA: Explain Collaborative Filtering via Boosted Local Rank-One Matrix Approximation.- Spatiotemporal-Aware Region Recommendation with Deep Metric Learning.- On the Impact of the Length of Subword Vectors on Word Embeddings.- Using Dilated Residual Network to Model Distant Supervision Relation Extraction.- Modeling More Globally: A Hierarchical Attention Network via Multi-Task Learning for Aspect-Based Sentiment Analysis.- A Sparse Matrix-based Join for SPARQL Query Processing.- Change Point Detection for Streaming High-Dimensional time series.- Demo Papers.- Distributed Query Engine for Multiple-Query Optimization over Data Stream.- Adding Value by Combining Business and Sensor Data: An Industry 4.0 Use Case.- AgriKG: An Agricultural Knowledge Graph and Its Applications.- KGVis: An Interactive Visual Query Language for Knowledge Graphs.- OperaMiner: Extracting Character Relations from Opera Scripts using Deep Neural Networks.- GparMiner: A System to mine Graph Pattern Association Rules.- A Data Publishing System Based on Privacy Preservation.- Privacy as a Service: Publishing Data and Models.- Dynamic Bus Route Adjustment Based on Hot Bus Stop Pair Extraction.- DHDSearch: A Framework for Batch Time Series Searching on MapReduce.- Bus Stop Refinement based on Hot Spot Extraction.- Adaptive Transaction Scheduling for Highly Contended Workloads.- IMOptimizer: An Online Interactive Parameter Optimization System based on Big Data.- Tutorial Papers.- Cohesive Subgraphs with Hierarchical Decomposition on Big Graphs.- Tracking User Behaviours: Laboratory-Based and In-The-Wild User S.- Mining Knowledge Graphs for Vision Tasks.- Enterprise Knowledge Graph From Specific Business Task to Enterprise Knowledge Management.- Knowledge Graph Data Management.- Deep learning for Healthcare Data Processing.

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Book SynopsisThe two-volume set of LNCS 11778 and 11779 constitutes the refereed proceedings of the 18th International Semantic Web Conference, ISWC 2019, held in Auckland, New Zealand, in October 2019. The ISWC conference is the premier international forum for the Semantic Web / Linked Data Community.The total of 74 full papers included in this volume was selected from 283 submissions. The conference is organized in three tracks: for the Research Track 42 full papers were selected from 194 submissions; the Resource Track contains 21 full papers, selected from 64 submissions; and the In-Use Track features 11 full papers which were selected from 25 submissions to this track.The chapter "The SEPSES knowledge graph: An integrated resource for cybersecurity" is open access under a CC BY 4.0 license at link.springer.com.Table of ContentsResources Track.- The KEEN Universe: An Ecosystem for Knowledge Graph Embeddings with a Focus on Reproducibility and Transferability.- VLog: A Rule Engine for Knowledge Graphs.- ArCo: the Italian Cultural Heritage Knowledge Graph.- Making Study Populations Visible through Knowledge Graphs.- LC-QuAD 2.0: A large dataset for complex question answering over Wikidata and DBpedia.- SEO: A Scientific Events Data Model.- DBpedia FlexiFusion - The Best of Wikipedia > Wikidata > Your Data.- The Microsoft Academic Knowledge Graph: A Linked Data Source with 8 Billion Triples of Scholarly Data.- The RealEstateCore Ontology.- FoodKG: Semantics-Driven Knowledge Graph for Food Recommendation.- BTC-2019: The 2019 Billion Triple Challenge Dataset.- Extending the YAGO2 Knowledge Graph with Precise Geospatial Knowledge.- The SEPSES knowledge graph: An integrated resource for cybersecurity.- SemanGit: A Linked Dataset from git.- Squerall: Virtual Ontology-Based Access to Heterogeneous and Large Data Sources.- List.MID: A MIDI-Based Benchmark for Evaluating RDF Lists.- A Scalable Framework for Quality Assessment of RDF Datasets.- QaldGen: Towards Microbenchmarking of Question Answering Systems Over Knowledge Graphs.- Sparklify: A Scalable Software Component for Efficient evaluation of SPARQL queries over distributed RDF datasets.- ClaimsKG: A Knowledge Graph of Fact-Checked Claims.- CoCoOn: Cloud Computing Ontology for IaaS Price and Performance Comparison.- In-Use Track.- Semantically-enabled Optimization of Digital Marketing Campaigns.- An End-to-end Semantic Platform For Nutritional Diseases Management.- VLX-Stories: building an online Event Knowledge Base with Emerging Entity detection.- Personalized Knowledge Graphs for the Pharmaceutical Domain.- Use of OWL and Semantic Web Technologies at Pinterest.- An Assessment of Adoption and Quality of Linked Data in European Open Government Data.- Easy Web API Development with SPARQL Transformer.- Benefit graph extraction from healthcare policies.- Knowledge Graph Embedding for Ecotoxicological Effect Prediction.- Improving Editorial Workflow and Metadata Quality at Springer Nature.- A Pay-as-you-go Methodology to Design and Build Enterprise Knowledge Graphs from Relational Databases.

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Book SynopsisThis book constitutes the refereed post-conference proceedings of the Special Event on the Analysis of Experimental Algorithms, SEA² 2019, held in Kalamata, Greece, in June 2019.The 35 revised full papers presented were carefully reviewed and selected from 45 submissions. The papers cover a wide range of topics in both computer science and operations research/mathematical programming. They focus on the role of experimentation and engineering techniques in the design and evaluation of algorithms, data structures, and computational optimization methods.

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Book SynopsisThis book gathers papers on interactive and collaborative mobile learning environments, assessment, evaluation and research methods in mobile learning, mobile learning models, theory and pedagogy, open and distance mobile learning, life-long and informal learning using mobile devices, wearables and the Internet of Things, game-based learning, dynamic learning experiences, mobile systems and services for opening up education, mobile healthcare and training, case studies on mobile learning, and 5G network infrastructure. Today, interactive mobile technologies have become the core of many—if not all—fields of society. Not only do the younger generation of students expect a mobile working and learning environment, but also the new ideas, technologies and solutions introduced on a nearly daily basis also boost this trend. Discussing and assessing key trends in the mobile field were the primary aims of the 13th International Conference on Interactive Mobile Communication Technologies and Learning (IMCL2019), which was held in Thessaloniki, Greece, from 31 October to 01 November 2019. Since being founded in 2006, the conference has been devoted to new approaches in interactive mobile technologies, with a focus on learning. The IMCL conferences have since become a central forum of the exchange of new research results and relevant trends, as well as best practices. The book’s intended readership includes policymakers, academics, educators, researchers in pedagogy and learning theory, schoolteachers, further education lecturers, practitioners in the learning industry, etc.Table of ContentsA Framework for Developing a Set of Games for Children Diagnosed with Autism.- LinkLearn: Blockchain Technology As A Learning Tool.- New Era of the Nano-Electronic Devices – One of the Most Adaptive Learning Areas for the Next Period.- Open Source Online Conference System for Industry Experts Participation in Education.- Interactive TV And Music Education - Mobile Music Practices & Limitations: Learning Byzantine Music Online.- An Analysis For The Identification Of Use And Development Of Game Design Strategies For Early Childhood Learners.- Measuring knowledge gains in an m-learning intervention: The case of ChildConnect South Africa.- Measuring Uptake and Engagement in an m-learning Intervention: The Case of ChildConnect South Africa.- Development of a Classroom Response System: a Web-based approach used in SEPT.- Analysis of the Perception of Students of the Autonomous University of Baja California Sur for the use of m-learning environments in higher education.- Work-in-Progress: Development of a Framework for Incorporating Usability aspects with Digital Didactical Design for Mobile/Tablet based Learning in Pre-primary Education.- Promoting Authentic Student Assessment For STEM Project-Based Learning Activities.- Predictive Modelling Concerning Mobile Learning Advance.- Assessing Early Grade Mathematics Learner Outcomes Using m-Learning.- Users’ and Experts’ Evaluation of TARGET: a Serious Game for Mitigating Performance Enhancement Culture in Youth.- Poster: Exploring the Educational Affordances of an Academic Eportfolio for Engineer Students Through a Self-Regulated Learning Framework.- M-health as a Tool in the Cognitive Flexibility of the Elderly.- Critical categorization of Android and IOS Applications available for STEAM Education inEarly Childhood.- Museum exhibits that interact with students’ mobile devices. The case of Hellenic Maritime Museum.- Can Elementary Students Design Interactions And Learning Representations Of Educational Apps? The We!Design!Fractions Participatory Approach.- Pedagogical Considerations for Mobile-based Augmented Reality Learning Environments.- Machine learning & Deep learning: Recent Overview in Medical Care.- Learn to Code, an Interactive Application to Promote Mobile Student-Centred Learning.- Emerging Technologies and Augmented Reality in the development of Learning and Human Potential.- Level of Digital Literacies among Austrian College Students Assessed with an Online Survey.- A Gamefied Educational Network for Collaborative Learning.- Dynamic Mobile Student Response Approach.- Poster: The Use of a Virtual Personal Assistant (FENNChat) as a Platform for Providing Automated Responses to ODL Students’ Queries at UNISA.- Poster: Proposal of an Intelligent Model for Remote Tutoring.- A Comparative Study Of Augmented Reality Platforms For Building Educational Mobile Applications.- M-Learning: Are We Ready to Go Mobile?.- Framework For Automatic VPN Access To Remotely Discovered Resources.- Poster: Learn To Love My Grandchild Design With The User.- Enhancing Second Language Listening Skills through Smartphones: A Case Study.- Exposing Rural Indian Students to Mobile Assisted Language Learning: A Case Study.- How Can Facebook Use In Education Be Realized As Crowdsourcing Of Learning? An Exploration Of Junior, Senior And Graduates Working Together.- Concept of Digital Competences In-service Training Systems.- Means of Cyber Security Aspects Studying in Maritime Specialists Education.- Evaluating a Coaching MOOC Course to Support Dual Career of Athletes.- LoRa Technology Benefits in Educational Institutes.- Three IoT Wearables in Five European Cities! Reality and Perception.- Work-in-Progress: Designing an E-coaching System for Chronic Heart Failure Patients.- A Comparative Examination of AR and Video in Delivering Assembly Instructions.- Wearable E-textile As A Narrative Mediator For Enhancing Empathy In Moral Development.- RSSI Fingerprinting Techniques for Indoor Localization Datasets.- Mulsemedia Data Representation Based on Multi-Image Concept.- Cryptographic Systems And Threats In E-commerce.- Work in Progress. SportSWARES, Towards an Intelligent Way of Physical Training.- Greek Traditional Dances Capturing and a Kinematic Analysis Approach of the Greek Traditional Dance "Syrtos" (Terpsichore Project).- Educational Mobile Applications On Computational Thinking And Programming For Children Under 8 Years Old.- Work in Progress: GameLet: Readers’ Theater In Media-based Gamification For Reading Skills.- Students’ Experiences of Learning Mathematics Through Games Design.- A Serious Game for Amplifying Awareness on Multimodal Teaching: Game Design and Usability Study.- Smart Citizens for Smart Cities – A User Engagement Protocol for Citizen Participation.- ADDventurous Rhythmical Planet: A 3D Rhythm-based Serious Game for Social Skills Development of Children with ADHD.- The Design and Development of a Game-based approach to Entrepreneurship Education.- Interactive Serious Games for Cultural Heritage: A real-time Bouzouki Simulator for exploring the History and Sounds of Rebetiko Music.- Mobile Technologies Serious Games for the development of social skills in children with Autism Spectrum Disorders, in enhanced with Socially Assistive Robots interventions.- Creating Magic-Matt, an Interface to Transform Video Games to a Sports Experience.- “The Greek Steelbook (TGS)”: The Home Of Steelbook Presentations.- Poster: Determining a Network and Pedagogical Efficient Approach to Learning in Disruptive Environments.- Escape Room Game for Learning Digital Electronics in Vocational Education and Training (VET).- The e-Facilitator as a Key Player for Interactive Dissemination of STEAM Resources for e-Learning via Webinar.- Work-In-Progress: Interactive Lab Manuals and Videos for a Unit Operations Course.- Towards a Learning Analytics Dashboard for Collaborative Conversational Agent Activities in MOOCs.- Interactive Educational Practices & Distance Learning: A Small Connection with Mobile Learning and the Challenges of Deregulation in Connectivity.- Automatic Source Code Generation From OWL Pseudocode.- Gamifying Early Foreign Language learning: using Digital Storytelling and Augmented Reality to enhance vocabulary learning.- Ontology-Based System For Automatic SQL Exercise Generation.- Augmented Reality Application Based on Information Barcoding.- Work in Progress: The Impact Of The Project Onboardmed On Development Of Study Courses In Maritime Emergency Management.- An Approach for Supporting Space Orientation of the Blind Using Ontologically-Based Object Map.- Soupa And Integration Of Ontologies Verl For Conceptualizing Context In Ubiquitous Computing.- A Small Robotic Step for the Therapeutic Treatment of Mental Illnesses: the Breakthrough in Routines and Behaviour.- The Use of Gamification in Evaluating Children's Emotional Intelligence.- Using Gamification Based on Mobile Platform in Therapeutic Interventions for Children With Dyslexia.- Combined Approach to Diagnose ADHD: Gamifying Conners Rating Scale.- Using Gamification Based on Virtual Reality Mobile Platform for Treatment of Adults with Amblyopia.- Linear Programming Model Applied To The Optimization Of Nutritional Diets For Athletes.- White Blood Cells Detection and Classification Using Convolutional Neural Network.- Work-in-Progress: The Use of Big Data And Data Analytics in the Prevention, the Diagnosis and the Monitoring of Long-term Diseases.- An Interactive Augmented Reality Volume Rendering Mobile Application.- Design of an Accessible Web Portal for the Labor Insertion of people with blindness.- MassiveLearning: online masterclass course.- Touch Gesture Performance of Kindergarten Children on E-learning Applications: A Case Study in Sri Lanka.- Learning Diaries–A Valuable Companion of Mobile Learning for Higher Education in Software Engineering.- Kabelo’s Everyday Digital Play An Analysis of a ‘Small and Thick’ Ethnographic Portrait.- Use Of The Fractal Analysis Of Non-stationary Time Series In Mobile Foreign Exchange Trading For M-learning.- Work-In-Progress: SMART-WATER, a Νovel Τelemetry and Remote Control System Infrastructure for the Management of Water Consumption in Thessaloniki.- Educational Robotics For Creating Tangible Simulations: A Mixed Reality Space For Learning The Day/night Cycle.- TimeTracker App: Facilitating Migrants’ Engagement in their Second Language Learning.- “School – University – Industry” Cooperation “Cypress – National Instruments CLUB”.- PerFECt: A Performative Framework to Establish and Sustain Onlife Communities and its Use to Design a Mobile App to Extend a Digital Storytelling Platform with New Capabilities.-Exploring Impact of Olfactory Stimuli on User Performance on Mobile Platforms.- Building A Virtualized Cybersecurity Lab - Using Industry Support, Academic Programs And Open Source Solution For Setting-Up A Virtualized Cybersecurity Lab.- Work-in-Progress: Developing a Master Programme for Specialists in Industry 4.0.- "Teachers’ Perceptions Towards The Use of Mobile Augmented Reality: The Case of Greek Educators".- NavMusApp: Exploring the Instrumental Continuum.- Developing Communities of Practice to Maximize the Usability and Impact of Clean Sport Education in Europe: IMPACT Project.- A Fiber Wireless A-RoF/IFoF Uplink Transmission of up to 0.6Gb/s User Data Rate over a 32-element 60GHz Beam-steering Antenna for 5G Fronthaul Networks.- An eHealth-Care Driven Perspective on 5G Networks and Infrastructure.- 25 Gb/s Colorless Transmitter Based on Reflective Electroabsorption Modulator for Ultra-dense WDM-PON Application.- Challenges of Using Phased Array Antennas in Commercial Backhaul Equipment at 26 GHz.- Towards Intelligent Multi-Access Edge Computing Using Machine Learning.- Performance Analysis of NB-IoT Random Access Channel.

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Book SynopsisThis book is a greatly supplemented translation from Portuguese, originally published in 2015. It discusses the most appropriate ways to create databases for research on history and other humanities, including an extensive debate about the usages that historians have made of computing since the 1950s. It has four chapters: the first is dedicated to theoretical and methodical questions about the usage of databases in history; the second is about technical issues; the third presents the concept of research engineering (how to improve research in groups); the last is about the construction of databases. The author states that the use of technology in research in history and humanities should be preceded and mediated by theories and methods which deal with these disciplines and not by technical issues. The historian must know how to think “correctly” in order to use the technological tools in an autonomous way. The book provides a background, demonstrating how theory, methodology, and technique are always articulated in historical research, and will appeal to history students and researchers.Table of ContentsIntroductionSome theoretical and methodical questions One craftsman, one operationDismantling things in a organized way On the shoulders of giants Some computer issues Data structureRelational databasesConceptual, logical and physical modelsPhysical model Visual aspects concerning databases Colors Technique and theory: everyday problems and practical decisions Update, standardize or maintain the original? Fuzzy informationResearch Engineering Initial surveyCollection Strategies Manual filling of the database Importing data (digital) Database administration Constructing databases: some concrete examples Source-centered databases Method-centered databases Conclusion Bibliografia

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Book SynopsisThis book presents covariance matrix estimation and related aspects of random matrix theory. It focuses on the sample covariance matrix estimator and provides a holistic description of its properties under two asymptotic regimes: the traditional one, and the high-dimensional regime that better fits the big data context. It draws attention to the deficiencies of standard statistical tools when used in the high-dimensional setting, and introduces the basic concepts and major results related to spectral statistics and random matrix theory under high-dimensional asymptotics in an understandable and reader-friendly way. The aim of this book is to inspire applied statisticians, econometricians, and machine learning practitioners who analyze high-dimensional data to apply the recent developments in their work.Table of ContentsForeword.- 1 Introduction.- 2 Traditional Estimators and Standard Asymptotics.- 3 Finite Sample Performance of Traditional Estimators.- 4 Traditional Estimators and High-Dimensional Asymptotics.- 5 Summary and Outlook.- Appendices.

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Book SynopsisThis book examines recent developments in Operations Management, and focuses on four major application areas: dynamic pricing, assortment optimization, supply chain and inventory management, and healthcare operations. Data-driven optimization in which real-time input of data is being used to simultaneously learn the (true) underlying model of a system and optimize its performance, is becoming increasingly important in the last few years, especially with the rise of Big Data. Table of ContentsPart 1: Generic Tools.- Chapter 1: The Stochastic Multi-armed Bandit Problem.- Chapter 2: Reinforcement Learning.- Chapter 3: Optimal Learning and Optimal Design.- Part 2: Price Optimization.- Chapter 4: Dynamic Pricing with Demand Learning: Emerging Topics and State of the Art.- Chapter 5: Learning and Pricing with Inventory Constraints.- Chapter 6: Dynamic Pricing and Demand Learning in Nonstationary Environments.- Chapter 7: Pricing with High-Dimensional Data.- Part 3: Assortment Optimization.- Chapter 8: Nonparametric Estimation of Choice Models.- Chapter 9: The MNL-Bandit Problem.- Chapter 10: Dynamic Assortment Optimization: Beyond MNL Model.- Part 4: Inventory Optimization.- Chapter 11: Inventory Control with Censored Demand.- Chapter 12: Joint Pricing and Inventory Control with Demand Learning.- Chapter 13: Optimization in the Small-Data, Large-Scale Regime.- Part 5: Healthcare Operations.- Chapter 14: Bandit Procedures for Designing Patient-Centric Clinical Trials.- Chapter 15: Dynamic Treatment Regimes.

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Book SynopsisThis book brings together the most recent, quality research papers accepted and presented in the 3rd International Conference on Artificial Intelligence and Applied Mathematics in Engineering (ICAIAME 2021) held in Antalya, Turkey between 1-3 October 2021. Objective of the content is to provide important and innovative research for developments-improvements within different engineering fields, which are highly interested in using artificial intelligence and applied mathematics. As a collection of the outputs from the ICAIAME 2021, the book is specifically considering research outcomes including advanced use of machine learning and careful problem designs on human-centred aspects. In this context, it aims to provide recent applications for real-world improvements making life easier and more sustainable for especially humans. The book targets the researchers, degree students, and practitioners from both academia and the industry.

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Book SynopsisThis book constitutes the refereed proceedings of the 13th International Conference onSubject-Oriented Business Process Management, S-BPM ONE 2022, held in Karlsruhe, Germany, during June 29–July 1, 2022.The 7 full papers and 4 short papers included in this book were carefully reviewed andselected from 15 submissions. They were organized in topical sections as follows: technology; application; and short papers.Table of Contents​Technology.- Privacy by Sharing Autonomy –A Design-Integrating Engineering Approach.- An Im- and Export Library for the Subject-Oriented Exchange Standard.- Understanding and Harnessing the Potential of Conversational AI for S-BPM.- Application.- A Comparative Study of Simulation Tools for Business Processes.- Subject-oriented process models of SMEs: Best practices and evaluation.- Business Process Management Bridging Marketing and IT: Transformation Model for Customer Journey Maps and BPMN.- A Co-Evolution Model of Collaborative Process Design.- Short Papers.- A Single Point of Contact for Privacy Management in Cyber-Physical Systems.- Subject-Oriented Modelling of VDI-4520: A First Draft.- Organizing a Self-Organized Team: Towards a Maturity Model for Agile Business Process Management.- Are BPM Practitioners and Researchers Friends? Current Questions of Process Professionals and the Impact of Science.

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Book SynopsisThis book constitutes the proceedings of the 4th Iberoamerican Conference and third Indo-American Conference on Knowledge Graphs and Semantic Web, KGSWC 2022, which took place in Madrid, Spain, in November 2022.The 22 full and 3 short research papers presented in this volume were carefully reviewed and selected from 63 submissions. The papers cover topics related to software and its engineering, software creation and management, Emerging technologies, Analysis and design of emerging devices and systems, Emerging tools and methodologies and others.Table of ContentsDBkWik++ - Multi Source Matching of Knowledge Graphs.- A Survey on Knowledge Graph-based Methods for Automated Driving.- Physicians’ Brain Digital Twin: Holistic Clinical & Biomedical Knowledge Graphs for Patient Safety and Value-Based Care to Prevent The Post-pandemic Healthcare Ecosystem Crisis.- Combining Ontology and Natural Language Processing methods for Prevention of Falls from Height.- Learning to Automatically Generating Genre-Specific Song Lyrics: A Comparative Study.- DLIME-Graphs: A DLIME Extension based on Triple Embedding for Graphs.- Edge-labelled graphs and property graphs - to the user, more similar than different.- Knowledge Graph supported machine parameterization for the injection moulding industry.- IPR: Integrative Policy Recommendation Framework based on Hybrid Semantics.- Convolutional Neural Networks applied to emotion analysis in texts: Experimentation from the Mexican context.- Proficient Annotation Recommendation in a Biomedical Content Authoring Environment.- DKMI: Diversification of Web Image Search using Knowledge Centric Machine Intelligence.- Does Wikidata Support Analogical Reasoning?.- Flexible Queries over Knowledge Graphs.- Knowledge Graphs for Community Detection in Textual Data.- Framework for Author Name Disambiguation in Scientific Papers Using an Ontological Approach and Deep Learning.- On contrasting and completing YAGO with GPT language models: an experiment for person-related attributes.- Easy and complex: new perspectives for metadata modeling using RDF-star and Named Graphs.- Multi-Aspect Sentiment Analysis using Domain Ontologies.- Popularity Driven Data Integration.- Methodology for Creating a Community Corpus using a Wikibase Knowledge Graph.- Understanding Human Activity Patterns in Smart Homes with Process Mining.- String Matching based Framework for Online Hindi Question Answering System.- Towards an ontological approach to business continuity assessment.- From Ontology to Knowledge Graph Trend: Ontology As Foundation Layer For Knowledge Graph.

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Book SynopsisThis book constitutes the proceedings of the International Joint Conference on Rules and Reasoning, RuleML+RR 2022, held in Berlin, Germany, during September 26–28, 2022. This is the 6th conference of a new series, joining the efforts of two existing conference series, namely “RuleML” (International Web Rule Symposium) and “RR” (Web Reasoning and Rule Systems). The 18 full research papers presented in this book were carefully reviewed and selected from 54 submissions. The papers cover the following topics: answer set programming; foundations of nonmonotonic reasoning; datalog; queries over ontologies; proofs, error-tolerance, and rules; as well as agents and argumentation.Table of ContentsAnswer Set Programming.- Foundations of Nonmonotonic Reasoning.- Datalog.- Queries Over Ontologies.- Proofs, Error-tolerance, and Rules.- Agents and Argumentation.

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Book SynopsisThis book constitutes selected and revised papers presented at the First International Conference on Electronic Governance with Emerging Technologies, EGETC 2022, held in Tampico, Mexico, in September 2022. The 15 full papers and 2 short papers presented were thoroughly reviewed and selected from the 54 submissions. This volume focuses on the recent developmentsin the domain of eGovernment and governance of digital organizations also aims to shed light on the emerging research trends and their applications. Table of ContentsDeep Learning based Obstructive Sleep Apnea Detection for e-health Applications.- Deep Learning and Sign language models based enhanced accessibility of e-governance services for speech and hearing-impaired.- A Blockchain Enabled Trusted Public Distribution Management System using Smart Contract.- Administration of Vaccine mechanism for COVID-19 using Blockchain.- Cyber Security Strategies While Safeguarding Information Systems in Public/Private Sectors.- The spatial relationships of meteorological data for Unmanned Aerial System Decision-Making Support.- HIAS: Hybrid Intelligence Approach for Soil Classification and Recommendation of Crops.- Education 5.0 Maturity Index: Concept and Prospects for Development.- Interpretability of AI systems in electronic governance to improve user trust and acceptance.- An automated stress recognition for digital healthcare: Towards e-governance.-Emergency supply chain management.- Public Budget Simulations with Machine Learning and Synthetic Data: Some Challenges and Lessons from The Mexican Case.- Open data hackathon as a tool for increased engagement of Generation Z: to hack or not to hack?.- Data Visualization Guide for Smart City Technologies.- A D eep Learning-based Mobile Chatbot Model for Crop Farmers.- Urban data: sources and targeted applications for urban planning indicators modelling.- Understanding KlimaDAO use and value: insights from an empirical analysis.

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Book SynopsisData science is a new field that touches on almost every domain of our lives, and thus it is taught in a variety of environments. Accordingly, the book is suitable for teachers and lecturers in all educational frameworks: K-12, academia and industry.This book aims at closing a significant gap in the literature on the pedagogy of data science. While there are many articles and white papers dealing with the curriculum of data science (i.e., what to teach?), the pedagogical aspect of the field (i.e., how to teach?) is almost neglected. At the same time, the importance of the pedagogical aspects of data science increases as more and more programs are currently open to a variety of people.This book provides a variety of pedagogical discussions and specific teaching methods and frameworks, as well as includes exercises, and guidelines related to many data science concepts (e.g., data thinking and the data science workflow), main machine learning algorithms and concepts (e.g., KNN, SVM, Neural Networks, performance metrics, confusion matrix, and biases) and data science professional topics (e.g., ethics, skills and research approach).Professor Orit Hazzan is a faculty member at the Technion’s Department of Education in Science and Technology since October 2000. Her research focuses on computer science, software engineering and data science education. Within this framework, she studies the cognitive and social processes on the individual, the team and the organization levels, in all kinds of organizations.Dr. Koby Mike is a Ph.D. graduate from the Technion's Department of Education in Science and Technology under the supervision of Professor Orit Hazzan. He continued his post-doc research on data science education at the Bar-Ilan University, and obtained a B.Sc. and an M.Sc. in Electrical Engineering from Tel Aviv University.Table of ContentsPart A - Overview of Data Science and Data Science Education1. Introductiona. How to use this bookb. Chapter overviews2. What is data science3. Introduction to data science educationa. Curriculum initiativesb. Data science education research4. Data science thinkinga. Computational thinkingb. Statistical thinkingc. Data thinkingd. Data literacyPart B - Challenges of Data Science Education5. The pedagogical challenge of data science education6. Data science education and the variety of learnersa. Data science as 21st century skillsb. Prerequisite knowledge for data sciencec. Data science for K-12d. Data science for undergraduatese. Data science for researchers: graduate studentsf. Data science for researchers: senior researchersg. Data science for industry7. The interdisciplinarity challengea. Multidisciplinarity, interdisciplinarity and transdisciplinarityb. Integration of the data domainc. Interdisciplinary pedagogyd. Interdisciplinary PCK (Pedagogical Content Knowledge)e. Interdisciplinary PBL (Project Based Learning)8. Data science skillsa. Professional skillsb. Soft skillsc. Research skillsPart C - Data science Teaching frameworks9. Teacher Preparation - the Method for Teaching Data Science course10. Data Science for Social Sciencea. Interdisciplinary CS1b. Machine learning for social science and digital humanities11. Conclusion

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Book SynopsisThis book constitutes selected papers presented during the two events: the First Forum, GranDGamesBR 2020, held in Recife, Brazil, in November 2020, and the Second Forum, GranDGamesBR 2021, held in Gramado, Brazi, in October 2021.The 12 papers presented were thoroughly reviewed and selected from 24 submissions. The topics included in this volume cover the following fields connected to games and entertainment computing: game design, educational games, games evaluation, game-based learning, player experience, human-computer interaction, games industry, business models, game software ecosystems, ethics, serious games, cyberdemocracy, emotional design, computer graphics, cognitive simulation, immersive entertainment, virtual/augmented/extended reality, gamification, and creative process.Table of ContentsChallenges in Evaluating Players’ Interaction with Digital Games.- The pursuit of fun in digital games: From the sandpit to the console and beyond.- Business Model for Indie Studios in Game Software Ecosystems.- Games as Mediating Platforms in an Open and Digital World.- Balancing Game Elements, Learning, and Emotions in Game Design.- Enhancing Students’ Learning Experience through Gamification: Perspectives and Challenges.- Ethics and Games, ethical games and ethics in game.- Challenges for XR in Games.- Stimulation of the Executive Functions Mediated by Digital Games: Current Challenges in the School Context.- Perceptual Analysis of Computer Graphics Characters in Digital Entertainment.- Nine Challenges for Immersive Entertainment.- Strategies to Promote Stakeholders’ Autonomy while Creating Educational Digital Games.

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Book SynopsisThis book focuses on differential privacy and its application with an emphasis on technical and application aspects. This book also presents the most recent research on differential privacy with a theory perspective. It provides an approachable strategy for researchers and engineers to implement differential privacy in real world applications.Early chapters are focused on two major directions, differentially private data publishing and differentially private data analysis. Data publishing focuses on how to modify the original dataset or the queries with the guarantee of differential privacy. Privacy data analysis concentrates on how to modify the data analysis algorithm to satisfy differential privacy, while retaining a high mining accuracy. The authors also introduce several applications in real world applications, including recommender systems and location privacy Advanced level students in computer science and engineering, as well as researchers and professionals working in privacy preserving, data mining, machine learning and data analysis will find this book useful as a reference. Engineers in database, network security, social networks and web services will also find this book useful.Table of ContentsPreliminary of Differential Privacy.- Differentially Private Data Publishing: Settings and Mechanisms.- Differentially Private Data Publishing: Interactive Setting.- Differentially Private Data Publishing: Non-interactive Setting.- Differentially Private Data Analysis.- Differentially Private Deep Learning.- Differentially Private Applications: Where to Start?.- Differentially Private Social Network Data Publishing.- Differentially Private Recommender System.- Privacy Preserving for Tagging Recommender Systems.- Differential Location Privacy.- Differentially Private Spatial Crowdsourcing.- Correlated Differential Privacy for Non-IID Datasets.- Future Directions.

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Book SynopsisThis book constitutes the thoroughly refereed post-conference proceedings of the Satellite Events of the 14th European Conference on the Semantic Web, ESWC 2017, held in Portoroz, Slovenia, in May/June2017.The volume contains 8 poster and 24 demonstration papers, selected from 105 submissions. Additionally, this book includes a selection of 13 best workshop papers. The papers cover various aspects of the semantic web.The chapter 'Scholia, Scientometrics and Wikidata' is available open access under a CC BY 4.0 license via link.springer.com.Table of ContentsQuerying the Web of Data.- Semantic Web Solutions for Large-Scale Biomedical Data Analytics.- Scientometrics.- RDF Stream Processing.- Emotions, Modality, Sentiment Analysis and the Semantic Web.- Applications of Semantic Web Technologies in Robotics.- Linked Data and Distributed Ledgers.- Linked Data Quality.- Semantic Deep Learning.- Humanities in the Semantic Web.

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Book SynopsisThe target of SQL tuning is the improvement of the existing execution plan. The authors discuss the removal of brakes in the execution plan. Such "brakes" or bottlenecks can be recognized by a formal analysis of the execution plan. For this purpose no data model knowledge is needed. This is especially beneficial for database administrators because they are usually not or insufficiently familiar with the data model. The book presents numerous practical examples with this method.Table of ContentsPrefaces.- Introduction.- Some thoughts concerning the term "SQL Tuning".- Minimum minimorum to the subject "Execution Plan".- Approaches to formal SQL Tuning.- Bottlenecks in Execution Plans.- Procedure for formal SQL Tuning.- Practical experiences with formal SQL Tuning.- Appendix.- Application of the formal method for analysis of performance issues following Oracle migration.

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Book SynopsisIn the era of propelling traditional energy systems to evolve towards smart energy systems, including power generation, energy storage systems, and electricity consumption have become more dynamic. The quality and reliability of power supply are impacted by the sporadic and rising use of electric vehicles, domestic loads, and industrial loads. Similarly, with the integration of solid state devices, renewable sources, and distributed generation, power generation processes are evolving in a variety of ways. Several cutting-edge technologies are necessary for the safe and secure operation of power systems in such a dynamic setting, including load distribution, automation, energy regulation & control, and energy trading. This book covers the applications of various big data analytics,artificial intelligence, and machine learning technologies in smart grids for demand prediction, decision-making processes, policy, and energy management. The book delves into the new technologies for modern power systems such as the Internet of Things, Blockchain for smart home and smart city solutions in depth.Technical topics discussed in the book include:• Hybrid smart energy system technologies• Smart meters• Energy demand forecasting• Use of different protocols and communication in smart energysystems• Power quality and allied issues and mitigation using AI• Intelligent transportation• Virtual power plants• AI based smart energy business models• Smart home solutions• Blockchain solutions for smart grids.Table of Contents1. Introduction to Smart Energy Systems and Recent Trends 2. An Overview of Artificial Intelligence, Big Data, and Internet of Things for Future Energy Systems 3. LoRa: A New Technology for Smart Grid Applications 4. Clustering Hybrid Application for Load Forecasting in Smart Grids 5. Big Data Analytical Techniques for Electrical Energy Forecasting in the Smart Grid Paradigm 6. A Review of Smart Grid Planning Approaches and a New Proposal for operational Planning of a Smart Grid with Smart Homes 7. Smart Meter Data Analytics Case Study: Identification of LV Distribution Network Topology to Design Optimal Planning Solutions 8. Build Smart Grids on Artificial Intelligence: A Real-world Example 9. Artificial Intelligence Enabled Energy-efficient Technologies for Secured Smart Homes 10. A Review of Technologies in Net Zero Energy Building for Islanded Operation

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Book SynopsisExplore the engineering mechanism of blockchain, cryptocurrency, and Ethereum. Know-how of peer-to-peer networks, IPFS, and decentralized databases. Explore the workings of DApps and build your own blockchain app.

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Book SynopsisThis book provides an overview of crowdsourced data management. Covering all aspects including the workflow, algorithms and research potential, it particularly focuses on the latest techniques and recent advances. The authors identify three key aspects in determining the performance of crowdsourced data management: quality control, cost control and latency control. By surveying and synthesizing a wide spectrum of studies on crowdsourced data management, the book outlines important factors that need to be considered to improve crowdsourced data management. It also introduces a practical crowdsourced-database-system design and presents a number of crowdsourced operators. Self-contained and covering theory, algorithms, techniques and applications, it is a valuable reference resource for researchers and students new to crowdsourced data management with a basic knowledge of data structures and databases.Table of Contents1. Introduction.- 2. Crowdsourcing Background. 3. Quality Control.- 4. Cost Control.- 5. Latency Control.- 6. Crowdsourcing Database Systems and Optimization.- 7. Crowdsourced Operators.- Conclusion.

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Book SynopsisPreface.- Acknowledgements.- Part 1. Communication over the Gaussian channel.- Chapter 1.Overview of the book.- Chapter 2. A statistical model for additive noise channels.- Chapter 3. Additive Gaussian noise model.- Problem Set 1.- Chapter 4. Optimal receiver: maximum A Posteriori (MAP) principle.- Chapter 5. Analysis of error probability.- Chapter 6. Multiple bits transmission via pulse amplitude modulation.- Problem Set 2.- Chapter 7. Multi-shot communication.- Chapter 8. Repetition coding.- Chapter 9: Capacity of the additive white Gaussian noise channel.- Problem Set 3.- Part 2. Communication over inter-symbol interference (ISI) channels.- Chapter 10. Signal conversion from discrete to continuous time (1/2).- Chapter 11. Signal conversion from discrete to continuous time (2/2).- Chapter 12. Optimal receiver architecture.- Problem Set 4.- Chapter 13. Optimal receiver in ISI channels: maximum likelihood (ML) sequence detection.- Chapter&nb

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Book SynopsisChapter 1 Introduction.- Chapter 2 Sets and Functions.- Chapter 3 Liner Algebra.- Chapter 4 Matrix Decomposition.- Chapter 5 Calculus.- Chapter 6 Advanced Calculus.- Chapter 7 Algorithms 1 – Principal Component Analysis.- Chapter 8 Algorithms 2 – Liner Regression.- Chapter 9 Algorithms 3 – Neural Networks.- Chapter 10 Probability.- Chapter 11 Further Probability.- Chapter 12 Elements of Statistics.- Chapter 13 Algorithms 4 – Maximum Likelihood Estimation and its Application to Regression.- Chapter 14 Data Modelling in Practice.

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Book SynopsisAt its core, machine learning is about efficiently identifying patterns and relationships in data. Many tasks, such as finding associations among terms so you can make accurate search recommendations or locating individuals within a social network who have similar interests, are naturally expressed as graphs. Graph-Powered Machine Learning introduces you to graph technology concepts, highlighting the role of graphs in machine learning and big data platforms. You’ll get an in-depth look at techniques including data source modeling, algorithm design, link analysis, classification, and clustering. As you master the core concepts, you’ll explore three end-to-end projects that illustrate architectures, best design practices, optimization approaches, and common pitfalls. Key Features · The lifecycle of a machine learning project · Three end-to-end applications · Graphs in big data platforms · Data source modeling · Natural language processing, recommendations, and relevant search · Optimization methods Readers comfortable with machine learning basics. About the technology By organizing and analyzing your data as graphs, your applications work more fluidly with graph-centric algorithms like nearest neighbor or page rank where it’s important to quickly identify and exploit relevant relationships. Modern graph data stores, like Neo4j or Amazon Neptune, are readily available tools that support graph-powered machine learning. Alessandro Negro is a Chief Scientist at GraphAware. With extensive experience in software development, software architecture, and data management, he has been a speaker at many conferences, such as Java One, Oracle Open World, and Graph Connect. He holds a Ph.D. in Computer Science and has authored several publications on graph-based machine learning.

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