Databases / Data management Books
APress Grow Your Business with AI
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.
£35.99
APress Building AI Driven Marketing Capabilities
Book SynopsisFrom understanding various technologies as an enabler to marketing efforts and its impact on decision making and mapping of various facets of customer experience, this book is recommended for marketers and learners to understand the advantages of using technology.Table of Contents1. From Data to Action: Leveraging AI in marketing1.1 AI & Marketing: Core Elements 1.2 Unleashing AI driven competitive advantage through IoT and Big Data Analytics1.3 Challenges of using AI technologies in the area of Marketing1.4 Core benefits of AI Marketing1.5 AI and future of Marketing 2. Informed Data driven decision making 2.1 Using Big data analytics for market intelligence2.2 Application of Big data analytics to marketing mix elements2.3 AI led Cognitive Data Quality Management2.4 AI-enabled marketing decisions 3. AI Marketing & Predicting Consumer Choices3.1 The value of social media for Improving Customer Engagement3.2 Optimizing marketing value, retention, customer satisfaction and loyalty3.3 Strategic applications of AI in different stages of customer journey3.4 AI in segmentation, targeting and positioning3.5 Internet trends and customer sentiment analysis 4. Unlocking Data in understanding Customers4.1 Customer Analytics4.1.1 Descriptive Customer Analytics4.1.2 Predictive Customer Analytics4.1.3 Prescriptive Customer Analytics4.2 Marketing Analytics: AI for Data Driven Marketing4.3 Customer Data Visualization & Information Management4.4 Mapping Customer Journey through big data analytics 5. Improving Experiences and Customer Satisfaction with AI5.1 AI and Product Life Cycle Management (PLM)5.2 Opportunities and Challenges of applying AI for PLM5.3 AI and granular personalization5.4 Use of AI to provide each segment of a target with tailored content 6. Value Creation & Value Capture with Artificial Intelligence6.1 Role of AI in optimizing Pricing6.2 Optimizing marketing value, retention and loyalty6.3 XR on value co-creation and customer engagement6.4 Creating value with data analytics6.5 Customer Value Modelling6.6 Marketing intelligence for optimal marketing return6.7 Creating value with data analytics 7. Reliable & Profitable AI driven Distribution7.1 Using AI for Distribution Process Management7.2 Smart Distribution7.3 Prediction of consumer behavior and improving lead generation7.4 Optimizing sales territory design with AI7.5 AI based delivery system7.6 AI integrated Logistics, inventory management, warehousing and transportation 8. Artificial Intelligence driven Promotions and Social Networking8.1 Network Modelling, Visualization and Analyzing Tools8.2 Role of Centrality in Social Networks: Influencer Marketing8.3 Sentiment Analysis and Public Opinion Mining8.4 Review Mining and Rating8.5 Big Data & scalability in Social Networks8.6 AI powered Chatbots and conversational experiences8.7 Propensity modelling for advertisement targeting and lead scoring8.8 Advertising Optimization & Viral Effects8.9 Fake News, Misinformation & Rumor Detection 9. Optimizing the future of Digital Marketing with A.I.9.1 Enhancing Interactive User Experience with AI9.2 Content Creation & Curation with AI9.3 Aligning marketing metrics with business goals9.4 Web analytics for digital marketing 10. Ethics of Artificial Intelligence for Marketing10.1 Dark side of AI in Marketing10.1.1 Consumers’ data protection rights10.1.2 Concerns about AI-enabled marketing decisions 10.1.3 Legal Concerns and Compliance issues10.2 Piracy, Security and Consumerism10.3 Ethical, Moral & Societal Challenges of AI 11. Case Studies on applications of AI11.1 AI driven cyber security and privacy11.2 Applications of AI in health care11.3 Applications of AI in tourism11.4 Applications of AI in manufacturing11.5 Applications of AI in finance
£42.49
O'Reilly Media Hadoop Security
Book SynopsisThis practical book not only shows Hadoop administrators and security architects how to protect Hadoop data from unauthorized access, it also shows how to limit the ability of an attacker to corrupt or modify data in the event of a security breach.
£29.99
O'Reilly Media Sharing Big Data Safely
Book SynopsisMany big data-driven companies today are moving to protect certain types of data against intrusion, leaks, or unauthorized eyes. But how do you lock down data while granting access to people who need to see it? In this practical book, authors Ted Dunning and Ellen Friedman offer two novel and practical solutions that you can implement right away.
£15.99
O'Reilly Media Feature Engineering for Machine Learning
Book SynopsisFeature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you'll learn techniques for extracting and transforming featuresthe numeric representations of raw datainto formats for machine-learning models.
£42.39
O'Reilly Media Moving Hadoop in the Cloud
Book SynopsisThis hands-on guide shows developers and systems administrators familiar with Hadoop how to install, use, and manage cloud-born clusters efficiently. You'll learn how to architect clusters that work with cloud-provider featuresnot just to avoid pitfalls, but also to take full advantage of these services.
£23.99
O'Reilly Media Seeking SRE
Book SynopsisInspired by Site Reliability Engineering, the successful O'Reilly book, this book explores a very different part of the SRE space. The more than two dozen chapters in Seeking SRE bring you into some of the important conversations going on in the SRE world right now.
£38.39
O'Reilly Media Learning GraphQL
Book SynopsisWith this practical guide, Alex Banks and Eve Porcello deliver a clear learning path for frontend web developers, backend engineers, and project and product managers looking to get started with GraphQL.
£27.74
O'Reilly Media Foundations for Architecting Data Solutions
Book SynopsisBig Data Solution Architecture provides everyone from CIOs and COOs to lead architects and lead developers with the fundamental concepts of big data development. Authors Ted Malaska and Jonathan Seidman guide you through all the major components necessary to start, architect, and develop successful big data projects.
£33.74
O'Reilly Media Google BigQuery The Definitive Guide
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.
£42.39
O'Reilly Media Kubeflow for Machine Learning
Book SynopsisThis guide helps data scientists build production-grade machine learning implementations with Kubeflow and shows data engineers how to make models scalable and reliable
£31.99
O'Reilly Media Building Serverless Applications with Google
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.
£42.39
O'Reilly Media The SelfService Data Roadmap
Book SynopsisData-driven insights are a key competitive advantage for any industry today, but deriving insights from raw data can still take days or weeks. With this practical book, data engineers, data scientists, and team managers will learn how to build a self-service data science platform that helps anyone in your organization extract insights from data.
£42.39
O'Reilly Media Data Algorithms with Spark
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.
£47.99
O'Reilly Media Machine Learning for Financial Risk Management
Book SynopsisFinancial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, and risk analysts will explore Python-based machine learning and deep learning models for assessing financial risk.
£53.99
O'Reilly Media Tableau Desktop Cookbook
Book SynopsisAuthor Lorna Brown provides more than 100 practical recipes to enhance the way you build Tableau dashboards--and helps you understand your data through the power of Tableau Desktop 2020's interactive data visualizations.
£47.99
O'Reilly Media Advancing into Analytics
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.
£42.39
SAP Press SAP HANA 2.0 Administration
Book SynopsisNovice or expert, this is your one-stop shop for administering SAP HANA 2.0! You’ll begin with a deep dive into database and engine architecture. Then explore your key tools: SAP HANA cockpit, SAP HANA Studio, the command-line interface, and more. From there, choose the topics you need.
£63.74
SAP Press SQLScript for SAP HANA
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.
£63.64
Springer-Verlag New York Inc. Handbook on Data Centers
Book SynopsisContributions from international, leading researchers and scholars offer topics in cloud computing, virtualization in data centers, energy efficient data centers, and next generation data center architecture.Table of ContentsEnergy-Efficient and High-Performance Processing of Large-Scale Parallel Applications in Data Centers.- Energy-Aware Algorithms for Task Graph Scheduling, Replica Placement, and Checkpoint Strategies.- Energy Efficiency in HPC Data Centers: Latest Advances to Build the Path to Exascale.- Techniques to Achieve Energy Proportionality in Data Centers: A Survey.- A Power-Aware Autonomic Approach for Performance Management of Scientific Applications in a Data Center Environment.- CoolEmAll: Models and Tools for Planning and Operating Energy Efficient Data Centers.- Smart Data Center.- Power and Thermal Efficient Numerical Processing.- Providing Green Services in HPC Data Centers: A Methodology Based on Energy Estimation.- Network Virtualization in Data Centers: A Data Plane Perspective.- Optical Data Center Networks: Architecture, Performance, and Energy Efficiency.- Scalable Network Communication Using Unreliable RDMA.- Packet Classification on Multi-Core Platforms.- Optical Interconnects for Data Center Networks.- TCP Congestion Control in Data Center Networks.- Routing Techniques in Data Center Networks.- Auditing for Data Integrity and Reliability in Cloud Storage.- I/O and File Systems for Data-Intensive Applications.- Cloud Resource Pricing Under Tenant Rationality.- Online Resource Management for Carbon-Neutral Cloud Computing.- A Big Picture of Integrity Verification of Big Data in Cloud Computing.- An Out-of-Core Task-Based Middleware for Data –Intensive Scientific Computing.- Building Scalable Software for Data Centers: An Approach to Distributed Computing at Enterprise Level.- Cloud Storage over Multiple Data Centers.- Realizing Accelerated Cost-Effective Distributed RAID.- Efficient Hardware-Supported Synchronization Mechanisms for Manycores.- Hardware Approaches to Transactional Memory in Chip Multiprocessors.- Data Center Modeling and Simulation Using OMNeT.- Power-Thermal Modeling and Control of Energy-Efficient Servers and Data Centers.- Modeling and Simulation of Data Center Networks.- C2Hunter: Detection and Mitigation of Covert Channels in Data Centers.- Selective and Private Access to Outsourced Data Centers.- Privacy in Data Centers: A Survey of Attacks and Countermeasures.- Quality-of-Service in Data Center Stream Processing for Smart City Applications.- Opportunistic Databank: A Context-Aware On-The-Fly Data Center for Mobile Networks.- Data Management: State-of-the-Practice at Open-Science Data Centers.- Data Summarization Techniques for Big Data: A Survey.- Central Management of Data Centers.- Monitoring of Data Centers Using Wireless Sensor Networks.- Network Intrusion Detection Systems in Data Centers.- Software Monitoring in Data Centers.- Usage Patterns in Multi-Tenant Data Centers: A Large-Case Field Study.- On Scheduling in Distributed Transactional Memory: Techniques and Tradeoffs.- Dependability-Oriented Resource Management Schemes for Cloud Computing Data Centers.- Resource Scheduling in Data-centric Systems.
£161.99
Taylor & Francis Inc Extremal Optimization
Book SynopsisExtremal Optimization: Fundamentals, Algorithms, and Applications introduces state-of-the-art extremal optimization (EO) and modified EO (MEO) solutions from fundamentals, methodologies, and algorithms to applications based on numerous classic publications and the authors' recent original research results. It promotes the movement of EO from academic study to practical applications. The book covers four aspects, beginning with a general review of real-world optimization problems and popular solutions with a focus on computational complexity, such as NP-hard and the phase transitions occurring on the search landscape.Next, it introduces computational extremal dynamics and its applications in EO from principles, mechanisms, and algorithms to the experiments on some benchmark problems such as TSP, spin glass, Max-SAT (maximum satisfiability), and graph partition. It then presents studies on the fundamental features of search dynamics and mechanisms in EO Table of ContentsFUNDAMENTALS, METHODOLOGY, AND ALGORITHMS. General Introduction. Introduction to Extremal Optimization. Extremal Dynamics-Inspired Self-Organizing Optimization. MODIFIED EO AND INTEGRATION OF EO WITH OTHER SOLUTIONS TO COMPUTATIONAL INTELLIGENCE. Modified Extremal Optimization. Memetic Algorithms with Extremal Optimization. Multiobjective Optimization with Extremal Dynamics. APPLICATIONS. EO for Systems Modeling and Control. EO for Production Planning and Scheduling. References.
£161.50
Taylor & Francis Inc Developing Essbase Applications
Book SynopsisMaintaining the advanced technical focus found in Developing Essbase Applications, this second volume is another collaborative effort by some of the best and most experienced Essbase practitioners from around the world.Developing Essbase Applications: Hybrid Techniques and Practices reviews technology areas that are much-discussed but still very new, including Exalytics and Hybrid Essbase. Covering recent improvements to the Essbase engine, the book illustrates the impact of new reporting and analysis tools and also introduces advanced Essbase best practices across a variety of features, functions, and theories.Some of this book's chapters are in the same vein as the previous volume: hardware, engines, and languages. Others cover new ground with Oracle Business Intelligence Enterprise Edition, design philosophy, benchmarking concepts, and multiple client tools. As before, these subjects are covered from both the technical and best practice perspectives.Trade Review"I enjoyed the first book because it was a collection of best practices, tips, tricks, and mini-guides. ... Reading this book, I was happy to find a wider representation of topics: brand new, forward-looking features like Hybrid Aggregation Mode; popular product offerings such as Exalytics and Oracle Business Intelligence Enterprise Edition integrations; topics relevant to many existing implementations; and even a representation of partner products."—Gabby Rubin, Senior Director, Product Management, Oracle Business AnalyticsTable of ContentsIntroduction. Essbase on Exalytics and the "Secret Sauce". Hybrid Essbase: Evolution or Revolution? The Young Person’s Guide to Essbase Cube Design. Essbase Performance and Load Testing. Utilizing Structured Query Language to Enhance Your Essbase Experience. Copernicus Was Right: Integrating Oracle Business Intelligence and Essbase. Managing Spreadsheets (and Essbase) Through Dodeca. Smart View Your Way.
£114.00
Chapman and Hall/CRC Exploratory Data Analysis Using R
Book SynopsisExploratory Data Analysis Using R provides a classroom-tested introduction to exploratory data analysis (EDA) and introduces the range of interesting - good, bad, and ugly - features that can be found in data, and why it is important to find them. It also introduces the mechanics of using R to explore and explain data. The book begins with a detailed overview of data, exploratory analysis, and R, as well as graphics in R. It then explores working with external data, linear regression models, and crafting data stories. The second part of the book focuses on developing R programs, including good programming practices and examples, working with text data, and general predictive models. The book ends with a chapter on keeping it all together that includes managing the R installation, managing files, documenting, and an introduction to reproducible computing. The book is designed for both advanced undergraduate, entry-level graduate students, and working professionals with little to no
£54.14
Taylor & Francis Inc Biometrics in a Data Driven World
Book SynopsisBiometrics in a Data Driven World: Trends, Technologies, and Challenges aims to inform readers about the modern applications of biometrics in the context of a data-driven society, to familiarize them with the rich history of biometrics, and to provide them with a glimpse into the future of biometrics.The first section of the book discusses the fundamentals of biometrics and provides an overview of common biometric modalities, namely face, fingerprints, iris, and voice. It also discusses the history of the field, and provides an overview of emerging trends and opportunities. The second section of the book introduces readers to a wide range of biometric applications. The next part of the book is dedicated to the discussion of case studies of biometric modalities currently used on mobile applications. As smartphones and tablet computers are rapidly becoming the dominant consumer computer platforms, biometrics-based authentication is emergingTable of ContentsIntroduction to Biometric Authentication. Challenges in Biometric Systems. Emerging trends and new opportunities in Biometrics. Biometrics in the mobile world. Biometric authentication techniques in the mobile platform. Case Studies of real-world mobile biometric systems. Biometrics in wearable technology and healthcare applications. Biometrics in Social Networks. Biometrics in Gaming Technologies. Biometrics in homeland security. Computational issues in biometrics. New directions in Biometrics research: What does the future hold?
£117.00
Taylor & Francis Inc A First Course in Machine Learning
Book SynopsisA First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility, starts from a detailed explanation of the basic foundations of Bayesian analysis in the simplest of settings, and goes all the way to the frontiers of the subject such as infinite mixture models, GPs, and MCMC.âDevdatt Dubhashi, Professor, Department of Computer Science and Engineering, Chalmers University, SwedenThis textbook manages to be easier to read than other comparable books in the subject while retaining all the rigorous treatment needed. The new chapters put it at the forefront of the field by covering topics that have become mainstream in machine learning over the last decade.âDaniel Barbara, George Mason University, Fairfax, Virginia, USAThe new edition of A First Course in Machine Learning by Rogers and Girolami is an excellent introductioTrade Review"I was impressed by how closely the material aligns with the needs of an introductory course on machine learning, which is its greatest strength. While there are other books available that aim for completeness, with exhaustively comprehensive introductions to every branch of machine learning, the book by Rogers and Girolami starts with the basics, builds a solid and logical foundation of methodology, before introducing some more advanced topics. The essentials of the model construction, validation, and evaluation process are communicated clearly and in such a manner as to be accessible to the student taking such a course. I was also pleased to see that the authors have not shied away from producing algebraic derivations throughout, which are for many students an essential part of the learning process—many other texts omit such details, leaving them as ‘an exercise for the reader.’ Being shown the explicit steps required for such derivations is an important part of developing a sense of confidence in the student. Overall, this is a pragmatic and helpful book, which is well-aligned to the needs of an introductory course and one that I will be looking at for my own students in coming months."—David Clifton, University of Oxford, UK"In my opinion, this is by far the best introduction to Machine Learning. It accomplishes something I would think impossible: it assumes essentially only high school mathematics and no statistics background, and yet, by introducing math, probability and statistics as needed, it manages to do an entirely rigorous introduction to Machine Learning. Proofs are not provided only for very few theorems; the book goes fairly deep and is really enjoyable to read. I told my students that this book will be one of the best investments they have ever made!"—Aleksandar Ignjatovic, University of New South Wales"The new edition of A First Course in Machine Learning by Rogers and Girolami is an excellent introductTable of ContentsLinear Modelling: A Least Squares Approach. Linear Modelling: A Maximum Likelihood Approach. The Bayesian Approach to Machine Learning. Bayesian Inference. Classification. Clustering. Principal Components Analysis and Latent Variable Models. Further Topics in Markov Chain Monte Carlo. Classification and Regression with Gaussian Processes. Dirichlet Process models.
£61.74
Chapman and Hall/CRC Data Stewardship for Open Science
Book SynopsisData Stewardship for Open Science: Implementing FAIR Principles has been written with the intention of making scientists, funders, and innovators in all disciplines and stages of their professional activities broadly aware of the need, complexity, and challenges associated with open science, modern science communication, and data stewardship. The FAIR principles are used as a guide throughout the text, and this book should leave experimentalists consciously incompetent about data stewardship and motivated to respect data stewards as representatives of a new profession, while possibly motivating others to consider a career in the field. The ebook, avalable for no additional cost when you buy the paperback, will be updated every 6 months on average (providing that significant updates are needed or avaialble). Readers will have the opportunity to contribute material towards these updates, and to develop their own data management plans, via the free Data Stewardship Wizard.
£48.44
Taylor & Francis Inc The Human Element of Big Data
Book SynopsisThe proposed book talks about the participation of human in Big Data.How human as a component of system can help in making the decision process easier and vibrant.It studies the basic build structure for big data and also includes advanced research topics.In the field of Biological sciences, it comprises genomic and proteomic data also. The book swaps traditional data management techniques with more robust and vibrant methodologies that focus on current requirement and demand through human computer interfacing in order to cope up with present business demand. Overall, the book is divided in to five parts where each part contains 4-5 chapters on versatile domain with human side of Big Data.Table of ContentsPrefaceEditorsContributorsSection I Introduction to the Human Element of Big Data: Definition, New Trends, and Methodologies1 Taming the Realm of Big Data Analytics: Acclamation or Disaffection?Audrey Depeige2 Fast Data Analytics Stack for Big Data AnalyticsSourav Mazumder3 Analytical Approach for Big Data in the Internet of ThingsAnand Paul, Awais Ahmad, and M. Mazhar Rathore4 Analysis of Costing Issues in Big DataKuldeep Singh Jadon and Radhakishan YadavSection II Algorithms and Applications of Advancement in Big Data5 An Analysis of Algorithmic Capability and Organizational ImpactGeorge Papachristos and Scott W. Cunningham6 Big Data and Its Impact on Enterprise ArchitectureMeena Jha, Sanjay Jha, and Liam O’Brien7 Supportive Architectural Analysis for Big DataUtkarsh Sharma and Robin Singh Bhadoria8 Clustering Algorithms for Big Data: A SurveyAnkita Sinha and Prasanta K. JanaSection III Future Research and Scope for the Human Element of Big Data9 Smart Everything: Opportunities, Challenges, and ImpactSiddhartha Duggirala10 Social Media and Big DataRichard Millham and Surendra Thakur11 Big Data Integration, Privacy, and SecurityRafael Souza and Chandrakant Patil12 Paradigm Shifts from E-Governance to S-GovernanceAkshi Kumar and Abhilasha SharmaSection IV Case Studies for the Human Element of Big Data: Analytics and Performance13 Interactive Visual Analysis of Traffic Big DataZhihan Lv, Xiaoming Li, Weixi Wang, Jinxing Hu, and Ling Yin14 Prospect of Big Data Technologies in HealthcareRaghavendra Kankanady and Marilyn Wells15 Big Data Suite for Market Prediction and Reducing Complexity Using Bloom FilterMayank Bhushan, Apoorva Gupta, and Sumit Kumar Yadav16 Big Data Architecture for Climate Change and Disease DynamicsDaphne Lopez and Gunasekaran ManogaranIndex
£114.00
Taylor & Francis Inc Data Mining
Book SynopsisData Mining: A Tutorial-Based Primer, Second Edition provides a comprehensive introduction to data mining with a focus on model building and testing, as well as on interpreting and validating results. The text guides students to understand how data mining can be employed to solve real problems and recognize whether a data mining solution is a feasible alternative for a specific problem. Fundamental data mining strategies, techniques, and evaluation methods are presented and implemented with the help of two well-known software tools. Several new topics have been added to the second edition including an introduction to Big Data and data analytics, ROC curves, Pareto lift charts, methods for handling large-sized, streaming and imbalanced data, support vector machines, and extended coverage of textual data mining. The second edition contains tutorials for attribute selection, dealing with imbalanced data, outlier analysis, time series analysis, mining textual data, and morTrade Review"Dr. Roiger does an excellent job of describing in step by step detail formulae involved in various data mining algorithms, along with illustrations. In addition, his tutorials in Weka software provide excellent grounding for students in comprehending the underpinnings of Machine Learning as applied to Data Mining. The inclusion of RapidMiner software tutorials and examples in the book is also a definite plus since it is one of the most popular Data Mining software platforms in use today."--Robert Hughes, Golden Gate University, San Francisco, CA, USATable of ContentsData Mining: A First View. Data Mining: A Closer Look. Basic Data Mining Techniques. Weka – A Tool for Knowledge Discovery.Pre Processing & Visualization Techniques. Knowledge Discovery in Databases. Formal Evaluation Techniques. The DataWarehouse. Neural Networks. Building Neural Networks with BpKNet. Statistical Methods. Specialized Techniques. A Case Studyin Knowledge Discovery. Rule-Based Systems. Managing Uncertainty in Rule-Based Systems. Intelligent Agents
£59.84
Taylor & Francis Inc Human Capital Systems Analytics and Data Mining
Book SynopsisHuman Capital Systems, Analytics, and Data Mining provides human capital professionals, researchers, and students with a comprehensive and portable guide to human capital systems, analytics and data mining. The main purpose of this book is to provide a rich tool set of methods and tutorials for Human Capital Management Systems (HCMS) database modeling, analytics, interactive dashboards, and data mining that is independent of any human capital software vendor offerings and is equally usable and portable among both commercial and internally developed HCMS.The book begins with an overview of HCMS, including coverage of human resource systems history and current HCMS Computing Environments. It next explores relational and dimensional database management concepts and principles. HCMS Instructional databases developed by the Author for use in Graduate Level HCMS and Compensation Courses are used for database modeling and dashboard design exercises. Trade ReviewUse the data, or ignore it at your peril. In the times we now live in, no company is without relevant, useful, actionable data on how we are performing, our staff, our suppliers, and many many more aspects of our businesses. If you don’t effectively monitor, analyse and interpret this data, then you are leaving yourself open to being overtaken by your rivals, who, you can be sure, are doing exactly this.This book thoroughly and methodically takes you through a series of ways in which you can, and should be using this data to your advantage. We’ve heard all the cliches and truisms about data being the new oil, but unless you know how to use it, then it could just be a meaningless pool of ones and zeroes to you. With a series of comprehensive and informative screenshots this book takes you through many areas in which the data can provide invaluable insights. It will be interesting to see if they decide to release video tutorials to accompany this book and the various chapters that they cover.This book seems to be a clear example that data analysts are becoming more and more important as so much of our working lives becomes digitised and leaving a digital footprint. A useful tool for those working at the coalface in this sector.-Simon Cocking, Irish Tech NewsTable of Contents1. Human Capital Management Systems 2. Human Capital Management System Components 3. Database Systems, Concepts and Design 4. Dimensional Modeling 5. Reporting and Analytics with Multidimensional and Relational Databases 6. Online Analytical Processing and the OLAP Cube Multidimensional Database 7. Multidimensional OLAP Database Project with SQL Server Analytical Services 8. Multidimensional Cube Analysis with Microsoft Excel and SQL Server Analysis Services 9. Data Mining 10. Project Management 11. Appendix A SQL Data Types 12. Appendix B SQL Database and Analysis Server Database Scripts 13. Appendix C Microsoft SQL Server Analytics Services Aggregation Options 14. Appendix D U. S. CDC Project Charter Template 15. Appendix E Sample HCMS Request for Information 16. Appendix F Human Capital Management System Request for Proposal (RFP) 17. Appendix G Sample HCMS Project Plan
£74.09
Taylor & Francis Inc Big Data Management and Processing
Book SynopsisFrom the Foreword:Big Data Management and Processing is [a] state-of-the-art book that deals with a wide range of topical themes in the field of Big Data. The book, which probes many issues related to this exciting and rapidly growing field, covers processing, management, analytics, and applications... [It] is a very valuable addition to the literature. It will serve as a source of up-to-date research in this continuously developing area. The book also provides an opportunity for researchers to explore the use of advanced computing technologies and their impact on enhancing our capabilities to conduct more sophisticated studies.---Sartaj Sahni, University of Florida, USABig Data Management and Processing covers the latest Big Data research results in processing, analytics, management and applications. Both fundamental insights and representative applications are provided. This book is a timely and valuable resource for students, researchers and seaTable of ContentsBig Data Management. Big Data Design, implementation, evaluation and services. Big Data as integration of technologies. Big Data analytics and visualization. Query processing and indexing. Elasticity for data management systems. Self-adaptive and energy-efficient mechanisms. Performance evaluation. Security, privacy, trust, data ownership and risk simulations. Processing. Techniques, algorithms and innovative methods of processing. Business and economic models. Adoption cases, frameworks and user evaluations. Data-intensive and scalable computing on hybrid infrastructures. MapReduce based computations. Many-Task Computing in the Cloud. Streaming and real-time processing. Big Data systems and applications for multidisciplinary applications.
£117.00
Taylor & Francis Inc Data Analytics Applications in Education
Book SynopsisThe abundance of data and the rise of new quantitative and statistical techniques have created a promising area: data analytics. This combination of a culture of data-driven decision making and techniques to include domain knowledge allows organizations to exploit big data analytics in their evaluation and decision processes. Also, in education and learning, big data analytics is being used to enhance the learning process, to evaluate efficiency, to improve feedback, and to enrich the learning experience.As every step a student takes in the online world can be traced, analyzed, and used, there are plenty of opportunities to improve the learning process of students. First, data analytics techniques can be used to enhance the student' s learning process by providing real-time feedback, or by enriching the learning experience. Second, data analytics can be used to support the instructor or teacher. Using data analytics, the instructor can better trace, and take targeted actions Table of ContentsIntroduction: Big Data Analytics in a Learning Environment. I. Data Analytics to Improve the Learning Process. Improved Student Feedback with Process and Data Analytics. Toward Data for Development: A Model on Learning Communities as a Platform for Growing Data Use. The Impact of Fraudulent Behavior on the Usefulness of Learning Analytics Applications: The Case of Question and Answer Sharing with Medium-Stakes Online Quizzing in Higher Education. II. Data Analytics to Measure Performance. Disentangling Faculty Efficiency from Students’ Effort. Using Data Analytics to Benchmark Schools: The Case of Portugal. The Use of Educational Data Mining Procedures to Assess Students’ Performance in a Bayesian Framework. Using Statistical Analytics to Study School Performance through Administrative Datasets. III. Policy Relevance and the Challenges Ahead. The Governance of Big Data in Higher Education. Evidence-Based Education and Its Implications for Research and Data Analytics with an Application to the Overeducation Literature.
£104.50
Taylor & Francis Inc NoSQL
Book SynopsisThis book discusses the advanced databases for the cloud-based application known as NoSQL. It will explore the recent advancements in NoSQL database technology. Chapters on structured, unstructured and hybrid databases will be included to explore bigdata analytics, bigdata storage and processing. The book is likely to cover a wide range of topics such as cloud computing, social computing, bigdata and advanced databases processing techniques.Table of ContentsDatabases and the Web. NoSQL Databases and Their Features. Hybrid Databases. Hardware Acceleration for Database Operations. Map-Reduced Architecture. Hadoop Architecture. Database Migration in NoSQL Databases. NoSQL as Bigdata Analytics Tools. Prospects of NoSQL for Bigdata Analytics. Research Trends in Cloud Database.
£114.00
Taylor & Francis Inc The Analytics Process
Book SynopsisThis book is about the process of using analytics and the capabilities of analytics in today's organizations. Cutting through the buzz surrounding the term analytics and the overloaded expectations about using analytics, the book demystifies analytics with an in-depth examination of concepts grounded in operations research and management science. Analytics as a set of tools and processes is only as effective as: The data with which it is working The human judgment applying the processes and understanding the output of these processes. For this reason, the book focuses on the analytics process. What is intrinsic to analytics' real organizational impact are the careful application of tools and the thoughtful application of their outcomes. This work emphasizes analytics as part of a process that supports decision-making within organizations. It wants to debunk overblown expectations that somehow analytics outputs or analytics as applied toTable of ContentsSECTION I. ANALYTICS PROCESS CONCEPTS. About the Analytics Process. Illustrating the Analytics Process through Risk Assessment. and Modeling. Analytics, Strategy, and Management Control Systems. SECTION II. ANALYTICS PROCESS APPLICATIONS. Data, Information, and Intelligence. The Rise of Big Data and Analytics in Higher Education. Google Analytics as a Prosumption Tool for Web Analytics. Knowledge-Based Cause–Effect Analysis Enriched by Generating Multilayered DSS Models. Online Community Projects in Lithuania: Cyber Security Perspective. Exploring Analytics in Health Information Delivery to Acute Health Care in Australia. Information Visualization and Knowledge Reconstruction of RFID Technology Translation in Australian Hospitals. Health Care Analytics and Big Data Management in Influenza Vaccination Programs: Use of Information–Entropy Approach. Sharing Knowledge or Just Sharing Data? Index.
£114.00
Taylor & Francis Inc Research Analytics
Book SynopsisThe growth of machines and users of the Internet has led to the proliferation of all sorts of data concerning individuals, institutions, companies, governments, universities, and all kinds of known objects and events happening everywhere in daily life. Scientific knowledge is not an exception to the data boom. The phenomenon of data growth in science pushes forth as the number of scientific papers published doubles every 915 years, and the need for methods and tools to understand what is reported in scientific literature becomes evident. As the number of academicians and innovators swells, so do the number of publications of all types, yielding outlets of documents and depots of authors and institutions that need to be found in Bibliometric databases. These databases are dug into and treated to hand over metrics of research performance by means of Scientometrics that analyze the toil of individuals, institutions, journals, countries, and even regions of the world. The Table of ContentsData Analytics and Scientometrics: Concepts, Methods, Synergies. The Growth of Scientific Knowledge. Scientometrics: Fundamentals and Applications. The Thomson Reuters Web of Science Database. The Elsevier SCOPUS Database. The Google Scholar Database. Institutional Repositories. The Academic Ranking of World Universities. The QS World University Ranking. The TIMES Higher Education World University Ranking. The US News Best Global University Rankings. Knowledge distribution through Web Domains: The Webometrics Ranking. The Image of Science: The SCIMAGO Ranking. Data Analytics and Scientometrics: Lessons Learned and Prospective Analysis.
£142.50
Taylor & Francis Inc Advances in Smart Cities
Book SynopsisThis is an edited book based on the selected submissions made to the conference titled International Conference in Smart Cities. The project provides an innovative and new approach to holistic management of cities physical, socio-economic, environmental, transportation and political assets across all domains, typically supported by ICT and open data.Table of ContentsAdoption and Acceptance of Mandatory Electronic Public Services by Citizens in the Developing World. Self-Sustainable Integrated Township. Smart People for Smart Cities. How Smart Cities influence Governance? Role of Manufacturing Sector to Develop Smart Economy. Concept of Smart Village in India. Smart City. Smart City Technologies. A Cloud-Based Mobile Application for Cashless Payments. Financial Viability of Energy Conservation using Natural Light. Information Risk for Digital Services. Mobile Commerce Research for Individual, Business and Society. The Shift Toward a Sustainable Urban Mobility through Decision Support Systems.
£133.00
Microsoft Press,U.S. Tabular Modeling in Microsoft SQL Server Analysis
Book SynopsisWith SQL Server Analysis Services 2016, Microsoft has dramatically upgraded its Tabular approach to business intelligence data modeling, making Tabular the easiest and best solution for most new projects. In this book, two world-renowned experts in Microsoft data modeling and analysis cover all you need to know to create complete BI solutions with these powerful new tools. Marco Russo and Alberto Ferrari walk you step-by-step through creating powerful data models, and then illuminate advanced features such as optimization, deployment, and scalability. Tabular Modeling in Microsoft SQL Server Analysis Services will be indispensable for everyone moving to Analysis Services Tabular, regardless of their previous experience with tabular-style models or with Microsoft's older Analysis Services offerings. It will also be an essential follow-up for every reader of the authors' highly-praised Microsoft SQL Server 2012 Analysis Services: The BISM Tabular Model.Table of Contents CHAPTER 1 Introducing the tabular model CHAPTER 2 Getting started with the tabular model CHAPTER 3 Loading data inside Tabular CHAPTER 4 Introducing calculations in DAX CHAPTER 5 Building hierarchies CHAPTER 6 Data modeling in Tabular CHAPTER 7 Tabular Model Scripting Language (TMSL) CHAPTER 8 The tabular presentation layer CHAPTER 9 Using DirectQuery CHAPTER 10 Security CHAPTER 11 Processing and partitioning tabular models CHAPTER 12 Inside VertiPaq CHAPTER 13 Interfacing with Tabular CHAPTER 14 Monitoring and tuning a Tabular service CHAPTER 15 Optimizing tabular models CHAPTER 16 Choosing hardware and virtualization
£33.37
Microsoft Press,U.S. MCSA SQL Server 2016 Database Development Exam
Book Synopsis
£43.19
Microsoft Press,U.S. Exam Ref 70-764 Administering a SQL Database
Book SynopsisPrepare for Microsoft Exam 70-764—and help demonstrate your real-world mastery of skills for database administration. This exam is intended for database administrators charged with installation, maintenance, and configuration tasks. Their responsibilities also include setting up database systems, making sure those systems operate efficiently, and regularly storing, backing up, and securing data from unauthorized access. Focus on the expertise measured by these objectives: • Configure data access and auditing • Manage backup and restore of databases • Manage and monitor SQL Server instances • Manage high availability and disaster recovery This Microsoft Exam Ref: • Organizes its coverage by exam objectives • Features strategic, what-if scenarios to challenge you • Assumes you have working knowledge of database installation, configuration, and maintenance tasks. You should also have experience with setting up database systems, ensuring those systems operate efficiently, regularly storing and backing up data, and securing data from unauthorized access. About the Exam Exam 70-764 focuses on skills and knowledge required for database administration. About Microsoft Certification Passing both Exam 70-764 and Exam 70-765 (Provisioning SQL Databases) earns you credit toward an MCSA: SQL 2016 Database Administration certification. See full details at: microsoft.com/learning Table of Contents 1. Configure Data Access and Auditing 2. Manage Backup and Restore of Databases 3. Manage and Monitor SQL Server Instances 4. Manage High Availability and Disaster Recovery
£23.59
Microsoft Press,U.S. Exam Ref 70-761 Querying Data with Transact-SQL
Book SynopsisPrepare for Microsoft Exam 70-761–and help demonstrate your real-world mastery of SQL Server 2016 Transact-SQL data management, queries, and database programming. Designed for experienced IT professionals ready to advance their status, Exam Ref focuses on the critical-thinking and decision-making acumen needed for success at the MCSA level. Focus on the expertise measured by these objectives: • Filter, sort, join, aggregate, and modify data • Use subqueries, table expressions, grouping sets, and pivoting • Query temporal and non-relational data, and output XML or JSON • Create views, user-defined functions, and stored procedures • Implement error handling, transactions, data types, and nulls This Microsoft Exam Ref: • Organizes its coverage by exam objectives • Features strategic, what-if scenarios to challenge you • Assumes you have experience working with SQL Server as a database administrator, system engineer, or developer • Includes downloadable sample database and code for SQL Server 2016 SP1 (or later) and Azure SQL Database Querying Data with Transact-SQL About the Exam Exam 70-761 focuses on the skills and knowledge necessary to manage and query data and to program databases with Transact-SQL in SQL Server 2016. About Microsoft Certification Passing this exam earns you credit toward a Microsoft Certified Solutions Associate (MCSA) certification that demonstrates your mastery of essential skills for building and implementing on-premises and cloud-based databases across organizations. Exam 70-762 (Developing SQL Databases) is also required for MCSA: SQL 2016 Database Development certification. See full details at: microsoft.com/learningTable of ContentsCHAPTER 1 Manage data with Transact-SQL CHAPTER 2 Query data with advanced Transact-SQL components CHAPTER 3 Program databases by using Transact-SQL
£23.59
Microsoft Press,U.S. Exam Ref 70-768 Developing SQL Data Models
Book SynopsisPrepare for Microsoft Exam 70-768–and help demonstrate your real-world mastery of Business Intelligence (BI) solutions development with SQL Server 2016 Analysis Services (SSAS), including modeling and queries. Designed for experienced IT professionals ready to advance their status, Exam Ref focuses on the critical thinking and decision-making acumen needed for success at the MCSA level. Focus on the expertise measured by these objectives: • Design a multidimensional BI semantic model • Design a tabular BI semantic model • Develop queries using Multidimensional Expressions (MDX) and Data Analysis Expressions (DAX) • Configure and maintain SSAS This Microsoft Exam Ref: • Organizes its coverage by exam objectives • Features strategic, what-if scenarios to challenge you • Assumes you are a database or BI professional with experience creating models, writing MDX or DAX queries, and using SSASTable of Contents1: Design a Multidimensional Business Intelligence (BI) Semantic ModelSkill 1.1. Create a Multidimensional Database by Using Microsoft SQL Server Analysis Services (SSAS)Skill 1.2. Design and Implement Dimensions in a CubeSkill 1.3. Implement Measures and Measure Groups in a Cube 2: Design a Tabular BI Semantic ModelSkill 2.1. Design and Publish a Tabular Data ModelSkill 2.2. Configure, Manage, and Secure a Tabular Model Skill 2.3. Develop a tabular model to access data in near real time 3: Develop Queries Using Multidimensional Expressions (MDX) and Data Analysis Expressions (DAX)Skill 3.1. Create Basic MDX QueriesSkill 3.2. Implement Custom MDX Solutions Skill 3.3. Create formulas by using the DAX language 4: Configure and Maintain SQL Server Analysis Services (SSAS)Skill 4.1. Plan and Deploy SSASSkill 4.2. Monitor and Optimize PerformanceSkill 4.3. Configure and Manage ProcessingSkill 4.4. Create Key Performance Indicators (KPIs) and Translations
£28.02
John Wiley and Sons Ltd Data Theory: Interpretive Sociology and
Book SynopsisThe datafication of our world offers huge challenges and opportunities for social science. The ‘data-drivenness’ of computational research can occur at the expense of theoretical reflection and interpretation. Additionally, it can be difficult to reconcile the ‘quantitative’ dimensions of big data with the ‘qualitative’ sensibilities needed for its understanding. At the same time, this opens up possibilities for reimagining key principles of social inquiry. In this experimental and provocative book, Simon Lindgren argues that a hybrid approach to data and theory must be developed in order to make sense of today's ambivalent, turbulent, and media-saturated political landscape. He pushes for the development of a critical science of data, joining the interpretive theoretical and ethical sensibilities of social science with the predictive and prognostic powers of data science and computational methods. In order for theories and research methods to be more useful and relevant, they must be dismantled and put together in new, alternative, and unexpected ways. Data Theory is essential reading for social scientists and data scientists, as well as students taking courses in social theory and data, digital methods, big data, and data and society.Trade Review�In this elegant book, Lindgren moves beyond the frequent schizophrenia of methods debates to ask: what happens when traditional social theory and data analytics are combined smartly? The result is illuminating and useful. Highly recommended!� Nick Couldry, London School of Economics and Political Science �This is a very interesting book with an original approach which will be useful to scholars and students.� Lina Dencik, Cardiff University �In this provocative text, Lindgren leads us on an innovative path that should both challenge and inspire researchers across the quant-qual divide. A new social science methods classic for the digital media era!� Sarah T. Roberts, UCLATable of ContentsIntroduction: Data Theory1 Beyond Method2 Decoding Social Forms3 Unintended Consequences4 Actor-Networks5 Collective Presentations6 Symbolic Power7 Theoretical I/O Conclusion: Theory/DataReferencesIndex
£45.00
John Wiley and Sons Ltd Data Theory: Interpretive Sociology and
Book SynopsisThe datafication of our world offers huge challenges and opportunities for social science. The ‘data-drivenness’ of computational research can occur at the expense of theoretical reflection and interpretation. Additionally, it can be difficult to reconcile the ‘quantitative’ dimensions of big data with the ‘qualitative’ sensibilities needed for its understanding. At the same time, this opens up possibilities for reimagining key principles of social inquiry. In this experimental and provocative book, Simon Lindgren argues that a hybrid approach to data and theory must be developed in order to make sense of today's ambivalent, turbulent, and media-saturated political landscape. He pushes for the development of a critical science of data, joining the interpretive theoretical and ethical sensibilities of social science with the predictive and prognostic powers of data science and computational methods. In order for theories and research methods to be more useful and relevant, they must be dismantled and put together in new, alternative, and unexpected ways. Data Theory is essential reading for social scientists and data scientists, as well as students taking courses in social theory and data, digital methods, big data, and data and society.Trade Review�In this elegant book, Lindgren moves beyond the frequent schizophrenia of methods debates to ask: what happens when traditional social theory and data analytics are combined smartly? The result is illuminating and useful. Highly recommended!� Nick Couldry, London School of Economics and Political Science �This is a very interesting book with an original approach which will be useful to scholars and students.� Lina Dencik, Cardiff University �In this provocative text, Lindgren leads us on an innovative path that should both challenge and inspire researchers across the quant-qual divide. A new social science methods classic for the digital media era!� Sarah T. Roberts, UCLATable of ContentsIntroduction: Data Theory 1 Beyond Method 2 Decoding Social Forms 3 Unintended Consequences 4 Actor-Networks 5 Collective Presentations 6 Symbolic Power 7 Theoretical I/O Conclusion: Theory/Data References Index
£15.19
Nova Science Publishers Inc A Closer Look at Big Data Analytics
Book SynopsisBig Data Analytics is a field that dissects, efficiently extricates data from, or in any case manages informational indexes that are excessively huge or complex to be managed by customary information preparing application programming. Information with numerous cases (lines) offers more noteworthy factual force, while information with higher multifaceted nature may prompt a higher bogus disclosure rate. Enormous information challenges incorporate catching information, information stockpiling, information investigation, search, sharing, move, representation, and questioning, refreshing, data security and data source. Large information was initially connected with three key ideas: volume, variety and velocity. Consequently, huge information regularly incorporates information with sizes that surpass the limit of conventional programming to measure inside a satisfactory time and worth. Current utilisation of the term enormous information will in general allude to the utilisation of predictive analytics, user behaviour analytics, or certain other progressed information investigation techniques that concentrate an incentive from information, and sometimes to a specific size of informational index. There is little uncertainty that the amounts of information now accessible are undoubtedly enormous, however that is not the most important quality of this new information biological system. Investigation of informational indexes can discover new relationships to spot business patterns or models. Researchers, business-persons, clinical specialists, promoting and governments consistently meet challenges with huge informational collections in territories including Internet look, fintech, metropolitan informatics, and business informatics. Researchers experience constraints in e-Science work, including meteorology, genomics, connectomics, complex material science reproductions, science and ecological exploration. The main objective of this book is to write about issues, challenges, opportunities, and solutions in novel research projects about big data in various domains. The topics of interest include, but are not limited to: efficient storage, management and sharing large scale of data; novel approaches for analysing data using big data technologies; implementation of high performance and/or scalable and/or real-time computation algorithms for analysing big data; usage of various data sources like historical data, social networking media, machine data and crowd-sourcing data; using machine learning, visual analytics, data mining, spatio-temporal data analysis and statistical inference in different domains (with large scale datasets); Legal and ethical issues and solutions for using, sharing and publishing large datasets; and the results of data analytics, security and privacy issues.Table of ContentsPreface; Artificial Intelligence for Knowing the Anticipation of Client from Online Food Delivery Using Big Data; An Overview on IOT Data Analytics with a Survey on CNN Accelerator Architecture; Big Data in Multi-Decision Making System of Aeronautic Industry; New Trends and Applications of Big Data Analytics for Medical Science and Healthcare; Deep Neural Networks in Bioinfomatics for MOTIF Identification; Utilizing Scratch to Creating Computational Thinking at School with Artificial Intelligence; Tracking System for Birds Migration using Sensors; Big Data Analytics Tools; Data Mining Techniques with its Applications; Design of Computationally Intelligent Decision Support System Using Data Analytics; Data Analytics using Computationally Intelligent Agents for Medical Diagnosis; Stability and Confidentiality Mechanism in Big Data; Index.
£163.19
BookBaby A Guide to Db2 Performance for Application Developers: Code for Performance from the Beginning
£34.49
O'Reilly Media Programming the Perl DBI
Book SynopsisOne of the greatest strengths of the Perl programming language is its ability to manipulate large amounts of data. Database programming is therefore a natural fit for Perl, not only for business applications but also for CGI-based web and intranet applications. The primary interface for database programming in Perl is DBI. DBI is a database-independent package that provides a consistent set of routines regardless of what database product you use--Oracle, Sybase, Ingres, Informix, you name it. The design of DBI is to separate the actual database drivers (DBDs) from the programmer's API, so any DBI program can work with any database, or even with multiple databases by different vendors simultaneously. Programming the Perl DBI is coauthored by Alligator Descartes, one of the most active members of the DBI community, and by Tim Bunce, the inventor of DBI. For the uninitiated, the book explains the architecture of DBI and shows you how to write DBI-based programs. For the experienced DBI dabbler, this book reveals DBI's nuances and the peculiarities of each individual DBD. The book includes: *An introduction to DBI and its design *How to construct queries and bind parameters *Working with database, driver, and statement handles *Debugging techniques *Coverage of each existing DBD *A complete reference to DBI This is the definitive book for database programming in Perl.Trade Review'The book is very well written with frequent examples. It certainly maintained my interest from beginning to end. I mirrored the authors' examples with my own MySQL databases and had no problems. I learnt SQL as well. If you need to interact with databases and you have access to Perl, then this book is a must.' - Mick Farmer, news@UK, June 2000Table of ContentsPreface. 1. Introduction From Mainframes to Workstations Perl DBI in the Real World A Historical Interlude and Standing Stones. 2. Basic Non-DBI Databases Storage Managers and Layers Query Languages and Data Functions Standing Stones and the Sample Database Flat-File Databases Putting Complex Data into Flat Files Concurrent Database Access and Locking DBM Files and the Berkeley Database Manager The MLDBM Module Summary. 3. SQL and Relational Databases The Relational Database Methodology Datatypes and NULL Values Querying Data Modifying Data Within Tables Creating and Destroying Tables. 4. Programming with the DBI DBI Architecture Handles Data Source Names Connection and Disconnection Error Handling Utility Methods and Functions. 5. Interacting with the Database Issuing Simple Queries Executing Non-SELECT Statements Binding Parameters to Statements Binding Output Columns do( ) Versus prepare( ) Atomic and Batch Fetching. 6. Advanced DBI Handle Attributes and Metadata Handling LONG/LOB Data Transactions, Locking, and Isolation. 7. ODBC and the DBI ODBC-Embraced and Extended DBI-Thrashed and Mutated The Nuts and Bolts of ODBC ODBC from Perl The Marriage of DBI and ODBC Questions and Choices Moving Between Win32::ODBC and the DBI And What About ADO? 8. DBI Shell and Database Proxying dbish-The DBI Shell Database Proxying A. DBI Specification B. Driver and Database Characteristics C. ASLaN Sacred Site Charter Index
£23.99
Taylor & Francis Inc Relational Management and Display of Site
Book SynopsisWhen your environmental project reaches the point where the amount of data seems overwhelming, you will need a robust tool to help you manage it. Written by a recognized expert and software author with over 25 years of industry experience, Relational Management and Display of Site Environmental Data begins with an overview of site data management concepts, then progresses through relational data management theory, the design of the database tool, and implementing a data management system. It includes detailed information on data output including mapping and GIS applications, practical suggestions about working with laboratories, and concludes with pitfalls, horror stories, and successes in site data management. Current topics such as Internet data delivery and eXtensible Markup Language (XML) are also covered.The text provides you with the skills needed to effectively implement and operate an environmental data management system. The concepts covered can be applied to any system, from stand-alone through client-server to Web-based. Relational Management and Display of Site Environmental Data combines the fundamentals of data management and display with the author's many years of experience to help you create your own data management system or make a better-informed decision when selecting a commercial solution.Table of ContentsOverview and Concepts. System Design and Implementation. Gathering Environmental Data. Maintaining the Data. Using the Data. Problems, Benefits, and Successes. Appendices: Needs Assessment Example. Data Model Example. Data Transfer Standard. The Parameters. Exercises. Glossary. Bibliography. Index.
£194.75
Oryx Press Inc Thesaurus of ERIC Descriptors, 14th Edition
£109.00
Artech House Publishers Knowledge Management in the Intelligence Enterprise
Book SynopsisThis system-level resource specifically applies knowledge management principles, practices and technologies to the intelligence domain. Designed for those responsible for the management of an intelligence enterprise operation and its delivery of reliable intelligence to key decision-makers, the text describes the essential principles of intelligence, from collection, processing and analysis to dissemination, for both national intelligence and business applications. The author aims to provide a balanced treatment of the organizational and architectural components of knowledge management, offering an understanding of the system infrastructure, tools and technologies necessary to implement the intelligence enterprise. He explores real-world applications and presents a detailed example of competitive intelligence unit design. Including over 80 illustrations, the volume offers a practical description of enterprise architecture design methodology, and covers the full range of national, military, business and competitive intelligence.Table of ContentsKnowledge management and intelligence; the intelligence enterprise; knowledge management processes; the knowledge-based intelligence organization; intelligence analysis and synthesis; implementing analysis-synthesis; knowledge internalization and externalization; explicit knowledge combination and transformation; the intelligence enterprise architecture; knowledge management technologies.
£118.25
