Expert systems / knowledge-based systems Books

Book SynopsisOverview of Supervised Learning.- Linear Methods for Regression.- Linear Methods for Classification.- Basis Expansions and Regularization.- Kernel Smoothing Methods.- Model Assessment and Selection.- Model Inference and Averaging.- Additive Models, Trees, and Related Methods.- Boosting and Additive Trees.- Neural Networks.- Support Vector Machines and Flexible Discriminants.- Prototype Methods and Nearest-Neighbors.- Unsupervised Learning.- Random Forests.- Ensemble Learning.- Undirected Graphical Models.- High-Dimensional Problems: p ? N.Trade ReviewFrom the reviews:"Like the first edition, the current one is a welcome edition to researchers and academicians equally…. Almost all of the chapters are revised.… The Material is nicely reorganized and repackaged, with the general layout being the same as that of the first edition.… If you bought the first edition, I suggest that you buy the second editon for maximum effect, and if you haven’t, then I still strongly recommend you have this book at your desk. Is it a good investment, statistically speaking!" (Book Review Editor, Technometrics, August 2009, VOL. 51, NO. 3)From the reviews of the second edition:"This second edition pays tribute to the many developments in recent years in this field, and new material was added to several existing chapters as well as four new chapters … were included. … These additions make this book worthwhile to obtain … . In general this is a well written book which gives a good overview on statistical learning and can be recommended to everyone interested in this field. The book is so comprehensive that it offers material for several courses." (Klaus Nordhausen, International Statistical Review, Vol. 77 (3), 2009)“The second edition … features about 200 pages of substantial new additions in the form of four new chapters, as well as various complements to existing chapters. … the book may also be of interest to a theoretically inclined reader looking for an entry point to the area and wanting to get an initial understanding of which mathematical issues are relevant in relation to practice. … this is a welcome update to an already fine book, which will surely reinforce its status as a reference.” (Gilles Blanchard, Mathematical Reviews, Issue 2012 d)“The book would be ideal for statistics graduate students … . This book really is the standard in the field, referenced in most papers and books on the subject, and it is easy to see why. The book is very well written, with informative graphics on almost every other page. It looks great and inviting. You can flip the book open to any page, read a sentence or two and be hooked for the next hour or so.” (Peter Rabinovitch, The Mathematical Association of America, May, 2012)Table of ContentsIntroduction.- Overview of supervised learning.- Linear methods for regression.- Linear methods for classification.- Basis expansions and regularization.- Kernel smoothing methods.- Model assessment and selection.- Model inference and averaging.- Additive models, trees, and related methods.- Boosting and additive trees.- Neural networks.- Support vector machines and flexible discriminants.- Prototype methods and nearest-neighbors.- Unsupervised learning.

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Book SynopsisThis comprehensive textbook teaches the fundamentals of database design, modeling, systems, data storage, and the evolving world of data warehousing, governance and more. Written by experienced educators and experts in big data, analytics, data quality, and data integration, it provides an up-to-date approach to database management. This full-color, illustrated text has a balanced theory-practice focus, covering essential topics, from established database technologies to recent trends, like Big Data, NoSQL, and more. Fundamental concepts are supported by real-world examples, query and code walkthroughs, and figures, making it perfect for introductory courses for advanced undergraduates and graduate students in information systems or computer science. These examples are further supported by an online playground with multiple learning environments, including MySQL, MongoDB, Neo4j Cypher, and tree structure visualization. This combined learning approach connects key concepts throughout the text to the important, practical tools to get started in database management.Trade Review'Although there have been a series of classical textbooks on database systems, the new dramatic advances call for an updated text covering the latest significant topics, such as big data analytics, No-SQL and much more. Fortunately, this is exactly what this book has to offer. It is highly desirable for training the next generation of data management professionals.' Jian Pei, Simon Fraser University, Canada'I haven't seen an as up-to-date and comprehensive textbook for Database Management as this one in many years. Principles of Database Management combines a number of classical and recent topics concerning Data Modeling, Relational Databases, Object-Oriented Databases, XML, Distributed Data Management, NoSQL and Big Data in an unprecedented manner. The authors did a great job in stitching these topics into one coherent and compelling story that will serve as an ideal basis for teaching both introductory and advanced courses.' Martin Theobald, University of Luxembourg'This is a very timely book with outstanding coverage of database topics and excellent treatment of database details. It not only gives very solid discussions of traditional topics like data modeling and relational databases but also contains refreshing contents on frontier topics such as XML databases, NoSQL databases, big data, and analytics. For those reasons, this will be a good book for database professionals who will keep using it for all stages of database studies and works.' J. Leon Zhao, City University of Hong Kong'This accessible, authoritative book introduces the reader the most important fundamental concepts of data management, while providing a practical view of recent advances. Both are essential for data professionals today.' Foster Provost, New York University, Stern School of Business'This guide to big and small data management addresses both fundamental principles and practical deployment. It reviews a range of databases and their relevance for analytics. The book is useful to practitioners because it contains many case studies, links to open-source software, and a very useful abstraction of analytics that will help them better choose solutions. It is important to academics because it promotes database principles which are key to successful and sustainable data science.' Sihem Amer-Yahia, Laboratoire d'Informatique de Grenoble and Editor-in-Chief the International Journal on Very Large DataBases'This book covers everything you will need to teach in a database implementation and design class. With some chapters covering big data, analytic models/methods, and No-SQL, it can keep our students up-to-date with these new technologies in data management related topics.' Han-fen Hu, University of Nevada, Las Vegas'As we are entering a new technological era of intelligent machines powered by data-driven algorithms, understanding fundamental concepts of data management and their most current practical applications has become more important than ever. This book is a timely guide for anyone interested in getting up to speed with the state of the art in database systems, big data technologies, and data science. It is full of insightful examples and case studies with direct industrial relevance.' Nesime Tatbul, Intel Labs and Massachusetts Institute of Technology'It is a pleasure to study this new book on database systems. The book offers a fantastically fresh approach to database teaching. The mix of theoretical and practical contents is almost perfect, the content is up-to-date and covers the recent ones, the examples are nice, and the database testbed provides an excellent way of understanding the concepts. Coupled with the authors 'expertise, this book is an important addition to the database field.' Arnab Bhattacharya, Indian Institute of Technology, Kanpur'Principles of Database Management is my favorite textbook for teaching a course on database management. Written in a well-illustrated style, this comprehensive book covers essential topics in established data management technologies and recent discoveries in data science. With a nice balance between theory and practice, it is not only an excellent teaching medium for students taking information management and/or data analytics courses, but also a quick and valuable reference for scientists and engineers working in this area.' Chuan Xiao, Graduate School of Informatics, Nagoya University'Data science success stories and big data applications are only possible because of advances in database technology. This book provides both a broad and deep introduction to databases. It covers the different types of database systems (from relational to noSQL) and manages to bridge the gap between data modeling and the underlying basic principles. The book is highly recommended for anyone that wants to understand how modern information systems deal with ever-growing volumes of data.' Wil van der Aalst, RWTH Aachen University'The database field has been evolving for several decades and the need for updated textbooks is continuous. Now, this need is covered by this fresh book by Lemahieu, van den Broucke and Baesens. It spans from traditional topics - such as the relational model and SQL - to more recent topics – such as distributed computing with Hadoop and Spark as well as data analytics. The book can be used as an introductory text and for graduate courses.' Yannis Manolopoulos, Data Science & Engineering Lab, Aristotle University of Thessaloniki'I like the way the book covers both traditional database topics and newer material such as big data, No-SQL databases, and data quality. The coverage is just right for my course and the level of the material is very appropriate for my students. The book also has clear explanations and good examples.' Barbara Klein, University of MichiganThis book provides a unique perspective on database management and how to store, manage, and analyze small and big data. The accompanying exercises and solutions, cases, slides, and YouTube lectures turn it into an indispensable resource for anyone teaching an undergraduate or postgraduate course on the topic.' Wolfgang Ketter, Erasmus University Rotterdam'This is a very modern textbook that fills the needs of current trends without sacrificing the need to cover the required database management systems fundamentals.' George Dimitoglou, Hood College, Maryland'This book is a much needed foundational piece on data management and data science. The authors successfully integrate the fields of database technology, operations research and big data analytics, which have often been covered independently in the past. A key asset is its didactical approach that builds on a rich set of industry examples and exercises. The book is a must-read for all scholars and practitioners interested in database management, big data analytics and its applications.' Jan Mendling, Institute for Information Business, ViennaTable of ContentsPreface; Part I. Databases and Database Design: 1. Fundamental concepts of database management; 2. Architecture and categorization of DBMSs; 3. Conceptual data modeling using the (E)ER model and UML class diagram; 4. Organizational aspects of data management; Part II. Types of Database Systems: 5. Legacy databases; 6. Relational databases: the relational model; 7. Relational databases: structured query language (SQL); 8. Object oriented databases and object persistence; 9. Extended relational databases; 10. XML databases; 11. NoSQL databases; Part III. Physical Data Storage, Transaction Management, and Database Access: 12. Physical file organization and indexing; 13. Physical database organization; 14. Basics of transaction management; 15. Accessing databases and database APIs; 16. Data distribution and distributed transaction management; Part IV. Data Warehousing, Data Governance and (Big) Data Analytics: 17. Data warehousing and business intelligence; 18. Data integration, data quality and data governance; 19. Big data; 20. Analytics; Appendix A. Cases and questions; Appendix B. Using the online environment; Appendix C. Answer key to select review questions; Glossary; Index.

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Book SynopsisHow do organizations become created? Entrepreneurship scholars have debated this question for decades, but only recently have they been able to gain insights into the non-linear dynamics that lead to organizational emergence, through the use of the complexity sciences. Written for social science researchers, Generative Emergence summarizes these literatures, including the first comprehensive review of each of the 15 complexity science disciplines. In doing so, the book makes a bold proposal for a discipline of Emergence, and explores one of its proposed fields, namely Generative Emergence. The book begins with a detailed summary of its underlying science, dissipative structures theory, and rigorously maps the processes of order creation discovered by that science to identify a 5-phase model of order creation in entrepreneurial ventures. The second half of the book presents the findings from an experimental study that tested the model in four fast-growth ventures through a year-long, weTable of ContentsChapter 1. Why Emergence ; Chapter 2. Prototypes of Emergence ; Chapter 3. Methods for Studying Emergence - 15 Fields of Complexity Science ; Chapter 4. Defining Emergence and Generative Emergence ; Chapter 5. Types of Emergence Studies ; Chapter 6. Dissipative Structures ; Chapter 7. Applications to Organizations ; Chapter 8. Introducing Dynamic States ; Chapter 9. Outcomes of Generative Emergence ; Chapter 10. Introducing the Five-Phase Process Model Of Generative Emergence ; Chapter 11. Phase 2 - Stress & Experiments ; Chapter 12. Phase 3 - Amplification and Critical Events ; Chapter 13. Phase 4 - New Order through Recombination ; Chapter 14. Phase 5 - Stabilizing Feedback ; Chapter 15. Cycles of Emergence ; Chapter 16. Cycles of Re-Emergence ; Chapter 17. Boundaries of Emergence, and Beyond the Boundaries ; Chapter 18. Enacting Emergence

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Book SynopsisTable of Contents1. Artificial Intelligence enabled Internet of Medical Things for Enhanced Healthcare Systems 2. Integrating Sensor, Actuators and IoT for for Smart Healthcare in Post COVID-19 World 3. Voice Signal based Disease Diagnosis using IoT and Learning Algorithms for Healthcare 4. Intelligent and sustainable approaches for medical big data management 5. A Predictive method for Emotional Sentiment Analysis by Machine Learning from EEG of Brainwave Data 6. Role of AI and IoT based medical diagnostics smart health care system for post-Covid-19 world 7. Windowed Modified Discrete Cosine Transform based Textural Descriptor approach for Voice Disorder Detection 8. Internet of Medical Things for Abnormality detection in Infants using Mobile Phone App with cry Signal Analysis 9. IoT based Effective Wearable Healthcare Monitoring System for Remote Areas 10. Blockchain for Transparent, Privacy Preserved and Secure Health Data Management 11. SPSHS: Security and Privacy Concerns in Smart Healthcare System

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Book SynopsisComputational inference is based on an approach to statistical methods that uses modern computational power to simulate distributional properties of estimators and test statistics.Trade ReviewFrom the reviews:“This is a book that covers many of the computational issues that statisticians will encounter as part of their research and applied work. … The writing in the book is quite clear and the author has done a good job providing the essence of each topic. … Overall, I think this is an excellent book. … This book will give a graduate student a good overview of the field. There are exercises provided for each chapter together with some solutions.” (Michael J. Evans, Mathematical Reviews, Issue 2011 b)“This book is a superior treatment of the important subject of statistical computing. I strongly recommend this book to anyone who analyzes data using either a commercial statistical software package or statistical computer programs written by the user or someone else. Thus this book is important not only for data oriented statisticians but for econometricians, psychometricians, political methodologists and biometricians as well. … All terms in this work including computing terms are clearly defined.” (Melvin Hinich, Technometrics, Vol. 53 (1), February, 2011)“I greatly appreciated the author’s command of both numerical and statistical computing … . The book also contains many exercises that substantiate the concepts, with solutions and hints in the appendix, an extensive bibliography, and a link to further literature and notes. The target readership includes undergraduates, postgraduates in statistics and allied fields such as computer science and mathematics, scientific research workers, and practitioners of statistics and numerical techniques. … I strongly recommend it for all scientific libraries.” (Soubhik Chakraborty, ACM Computing Reviews, October, 2010)“This book has a very large scope in that … it covers the dual fields of computational statistics and of statistical computing. … must-read for all students and researchers engaging into any kind of serious statistical programming. … is well-written, in a lively and personal style. … a reference book that should appear in the shortlist of any computational statistics/statistical computing graduate course as well as on the shelves of any researchers supporting his or her statistical practice with a significant dose of computing backup.”­­­ (Christian P. Robert, Statistical and Computation, Vol. 21, 2011)Table of ContentsPreliminaries.- Mathematical and Statistical Preliminaries.- Statistical Computing.- Computer Storage and Arithmetic.- Algorithms and Programming.- Approximation of Functions and Numerical Quadrature.- Numerical Linear Algebra.- Solution of Nonlinear Equations and Optimization.- Generation of Random Numbers.- Methods of Computational Statistics.- Graphical Methods in Computational Statistics.- Tools for Identification of Structure in Data.- Estimation of Functions.- Monte Carlo Methods for Statistical Inference.- Data Randomization, Partitioning, and Augmentation.- Bootstrap Methods.- Exploring Data Density and Relationships.- Estimation of Probability Density Functions Using Parametric Models.- Nonparametric Estimation of Probability Density Functions.- Statistical Learning and Data Mining.- Statistical Models of Dependencies.

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Book Synopsis Recommended by Bill Gates A thought-provoking and wide-ranging exploration of machine learning and the race to build computer intelligences as flexible as our own In the world's top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner--the Master Algorithm--and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible.

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Book SynopsisPresents the motivations for, intuitions behind, and basic mathematical models of natural spoken language communication. This book offers an overview of various aspects of the problem from the physics of speech production through the hierarchy of linguistic structure and ending with some observations on language and mind.Trade Review"...a succinct presentation of the most important mathematical technology of speech technology and the author's ideas for overcoming the limitations of these techniques…" (Mathematical Reviews, 2005j)Table of ContentsAuthor's preface. 1 Introduction 2 Preliminaries 2.1 The physics of speech production 2.2 The source-filter model 2.3 Information-bearing features of the speech signal 2.4 Time-frequency representations 2.5 Classifications of acoustic patterns in speech 2.6 Temporal invariance and stationarity 2.7 Taxonomy of linguistic structure 3 Mathematical models of linguistic structure 3.1 Probabilistic functions of a discrete Markov process 3.2 Formal grammars and abstract automata 4 Syntactic analysis 4.1 Deterministic parsing algorithms 4.2 Probabilistic parsing algorithms 4.3 Parsing natural language 5 Grammatical inference 5.1 Exact inference and Gold's theorem 5.2 Baum's algorithm for regular grammars 5.3 Event counting in parse trees 5.4 Baker's algorithm for context-free grammars 6 Information-theoretic analysis of speech communication 6.1 The Miller et al. experiments 6.2 Entropy of an information source 6.3 Recognition error rates and entropy 7 Automatic speech recognition and constructive theories of language 7.1 Integrated architectures 7.2 Modular architectures 7.3 Parameter estimation from fluent speech 7.4 System performance 7.5 Other speech technologies 8 Automatic speech understanding and semantics 8.1 Transcription and comprehension 8.2 Limited domain semantics 8.3 The semantics of natural language 8.4 System architectures 8.5 Human and machine performance 9 Theories of mind and language 9.1 The challenge of automatic natural language understanding 9.2 Metaphors for mind 9.3 The artificial intelligence program 10 A speculation on the prospects for a science of the mind 10.1 The parable of the thermos bottle: measurements and symbols 10.2 The four questions of science 10.3 A constructive theory of the mind 10.4 The problem of consciousness 10.5 The role of sensorimotor function, associative memory and reinforcement learning in automatic acquisition of spoken language by an autonomous robot 10.6 Final thoughts: predicting the course of discovery

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Book SynopsisThe book is the complete introduction and applications guide to this new technology. This book introduces the reader to features and gives an overview of geometric modeling techniques, discusses the conceptual development of features as modeling entities, illustrates the use of features for a variety of engineering design applications, and develops a set of broad functional requirements and addresses high level design issues.Table of ContentsBACKGROUND. Geometric Modeling. FUNDAMENTALS. Feature Concepts. Feature Creation Techniques. APPLICATION OF FEATURES. Features in Design. Features in Manufacturing. Feature Mapping and Data Exchange. DESIGN AND IMPLEMENTATION. Design-by-Features Techniques. Feature Recognition Techniques. Implementation Tools. Feature-Based Process Planning. BEYOND FEATURES. Future CAD/CAM Technologies. Appendices. Index.

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Book SynopsisThis book covers the development of intelligent systems using a mixture of scientific, philosophical, and engineering concepts. It provides an expert blend of theory and practice in intelligent systems design and uses real-world examples to illustrate technical concepts.Table of ContentsPreface. Emergence of a Theory. Knowledge. Perception. Goal Seeking and Planning. A Reference Model Architecture. Behavior Generation. World Modeling, Value Judgment, and Knowledge Representation. Sensory Processing. Engineering Unmanned Ground Vehicles. Future Possibilities. References. Index.

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Book SynopsisProvides an integrated view of the five kinds of enabling technologies in terms of knowledge media architectures such as: multimedia and hypermedia, object oriented GUI and visual programming, reusable component software and component integration, network publishing and electronic commerce, and object oriented and multimedia databases.Trade Review"…very interesting…recommended…" (E-Streams, Vol. 7, No. 4)Table of ContentsPreface. 1 Overview and Introduction. 1.1 Why Meme Media? 1.2 How Do Meme Media Change the Reuse of Web Contents? 1.3 How Do Meme Media Work? 1.4 Frequently Asked Questions and Limitations. 1.5 Organization of this Book. 2 Knowledge Media and Meme Media. 2.1 Introduction to Knowledge Media and Meme Media. 2.2 From Information Technologies to Media Technologies. 2.3 Summary. References. 3 Augmentation Media Architectures and Technologies—A Brief Survey. 3.1 History and Evolution of Augmentation Media. 3.2 History and Evolution of Knowledge-Media Architectures. 3.3 Meme Media and their Applications. 3.4 Web Technologies and Meme Media. 3.5 Summary. References. 4 An Outline of IntelligentPad and Its Development History. 4.1 Brief Introduction to IntelligentPad. 4.2 IntelligentPad Architecture. 4.3 Worldwide Marketplace Architectures for Pads. 4.4 End-User Computing and Media Toolkit System. 4.5 Open Cross-Platform Reusability. 4.6 Reediting and Redistribution by End-Users. 4.7 Extension toward 3D Representation Media. 4.8 Summary. References. 5 Object Orientation and MVC. 5.1 Object-Oriented System Architecture—A Technical Introduction. 5.2 Class Refinement and Prototyping. 5.3 Model, View, Controller. 5.4 Window Systems and Event Dispatching. 5.5 Summary. References. 6 Component Integration. 6.1 Object Reusability. 6.2 Components and Application Linkage. 6.3 Compound Documents and Object Embedding/Linking. 6.4 Generic Components. 6.5 What to Reuse—Components or Sample Compositions? 6.6 Reuses and Maintenance. 6.7 Integration of Legacy Software. 6.8 Distributed Component Integration and Web Technologies. 6.9 Summary. References. 7 Meme Media Architecture. 7.1 Current Megatrends in Computer Systems. 7.2 Primitive Media Objects. 7.3 Composition through Slot Connections. 7.4 Compound-Document Architecture. 7.5 Standard Messages between Pads. 7.6 Physical and Logical Events and their Dispatching. 7.7 Save and Exchange Format. 7.8 Copy and Shared Copy. 7.9 Global Variable Pads. 7.10 Summary. References. 8 Utilities for Meme Media. 8.1 Generic Utility Functions as Pads. 8.2 FieldPad for the Event Sharing. 8.3 StagePad for Programming User Operations. 8.4 Geometrical Management of Pads. 8.5 Proxy Pads to Assimilate External Objects. 8.6 Legacy Software Migration. 8.7 Special Effect Techniques. 8.8 Expression Pad. 8.9 Transformation Pads. 8.10 Summary. References. 9 Multimedia Application Framework. 9.1 Component Pads for Multimedia Application Frameworks. 9.2 Articulation of Objects. 9.3 Hypermedia Framework. 9.4 Summary. References. 10 IntelligentPad and Databases. 10.1 Relational Databases, Object-Oriented Databases, and Instance Bases. 10.2 Form Bases. 10.3 Pads as Attribute Values. 10.4 Multimedia Database. 10.5 Hypermedia Database. 10.6 Geographical Information Databases. 10.7 Content-Based Search and Context-Based Search. 10.8 Management and Retrieval of Pads. 10.9 Summary. References. 11 Meme Pool Architectures. 11.1 Pad Publication Repository and the WWW. 11.2 Pad Publication and Pad Migration. 11.3 Web Pages as Pad Catalog. 11.4 URL-Anchor Pads. 11.5 HTMLViewerPad with Embedded Arbitrary Composite Pads. 11.6 New Publication Media. 11.7 Annotation on Web Pages. 11.8 Piazza as a Meme Pool. 11.9 Reediting and Redistributing Web Content as Meme Media Objects. 11.10 Redistribution and Publication of Meme Media Objects as Web Content. 11.11 Summary. References. 12 Electronic Commerce for Pads. 12.1 Electronic Commerce. 12.2 From Pay-per-Copy to Pay-per-Use. 12.3 Digital Accounting, Billing, and Payment. 12.4 Ecology of Pads in the Market. 12.5 Superdistribution of Pads. 12.6 Pad Integration and Package Business. 12.7 Summary. References. 13 Spatiotemporal Editing of Pads. 13.1 Geometrical Arrangement of Pads. 13.2 Time-Based Arrangement of Pads. 13.3 Spatiotemporal Editing of Pads. 13.4 Information Visualization. 13.5 Summary. References. 14 Dynamic Interoperability of Pads and Workflow Modeling. 14.1 Dynamic Interoperability of Pads Distributed across Networks. 14.2 Extended Form-Flow System. 14.3 Pad-Flow Systems. 14.4 Dynamic Interoperability across Networks. 14.5 Workflow and Concurrent Engineering. 14.6 Summary. References. 15 Agent Media. 15.1 Three Different Meanings of Agents. 15.2 Collaborative-and-Reactive Agents and Pads. 15.3 Mobile Agents and Pads. 15.4 Pad Migration and Script Languages. 15.5 Summary. References. 16 Software Engineering with IntelligentPad. 16.1 IntelligentPad as Middleware. 16.2 Concurrent Engineering in Software Development. 16.3 Components and Their Integration. 16.4 Patterns and Frameworks in IntelligentPad. 16.5 From Specifications to a Composite Pad. 16.6 Pattern Specifications and the Reuse of Pads. 16.7 IntelligentPad as a Software Development Framework. 16.8 Summary. References. 17 Other Applications of IntelligentPad. 17.1 Capabilities Brought by the Implementation in IntelligentPad. 17.2 Tool Integration Environments and Personal Information Management. 17.3 Educational Applications. 17.4 Web Page Authoring. 17.5 Other Applications. 17.6 Summary. 18 3D Meme Media. 18.1 3D Meme Media IntelligentBox. 18.2 3D Application Systems. 18.3 IntelligentBox Architecture. 18.4 Example Boxes and Utility Boxes. 18.5 Animation with IntelligentBox. 18.6 Information Visualization with IntelligentBox. 18.7 Component-Based Framework for Database Reification. 18.8 Virtual Scientific Laboratory Framework. 18.9 3D Meme Media and a Worldwide Repository of Boxes as a Meme Pool. 18.10 Summary. References. 19 Organization and Access of Meme Media Objects. 19.1 Organization and Access of Intellectual Resources. 19.2 Topica Framework. 19.3 The Application Horizon of the Topica Framework. 19.4 Queries over the Web of Topica Documents. 19.5 Related Research. 19.6 Summary. References. 20 IntelligentPad Consortium and Available Software. 20.1 IntelligentPad Consortium. 20.2 Available Software. 20.3 Concluding Remarks. Author Index. Subject Index. About the Author.

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Book SynopsisIncluding contributions from leading experts in the field, this book covers applications and developments of heuristic search methods for solving complex optimization problems. The book covers various local search strategies including genetic algorithms, simulated annealing, tabu search and hybrids thereof.Table of ContentsPartial table of contents: Modern Heuristic Techniques. TECHNIQUES. Localized Simulated Annealing in Constraint Satisfaction andOptimization. Observing Logical Interdependencies in Tabu Search: Methods andResults. Reactive Search: Toward Self-Tuning Heuristics. Integrating Local Search into Genetic Algorithms. CASE STUDIES. Local Search for Steiner Trees in Graphs. Local Search Strategies for the Vehicle Fleet Mix Problem. A Tabu Search Algorithm for Some Discrete-Continuous SchedulingProblems. The Analysis of Waste Flow Data from Multi-Unit IndustrialComplexes Using Genetic Algorithms. The Evolution of Solid Object Designs Using GeneticAlgorithms. The Convoy Movement Problem with Initial Delays. A Brief Comparison of Some Evolutionary Optimization Methods. Index.

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Book SynopsisDescription logics are knowledge representation formalisms that are highly relevant in computer science, knowledge representation and the semantic web. This is the first introductory textbook published on the subject, suitable for self-study by graduate students and as teaching material for university courses.Table of Contents1. Introduction; 2. A basic DL; 3. A little bit of model theory; 4. Reasoning in DLs with tableau algorithms; 5. Complexity; 6. Reasoning in the εL family of description logics; 7. Query answering; 8. Ontology languages and applications; Appendix A. Description logic terminology; References; Index.

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Book SynopsisPreface.- Introduction.- Part 1: General Recommendation Techniques.- Trust Your Neighbors: A Comprehensive Survey of Neighborhood-based Methods for Recommender Systems (Desrosiers).- Advances in Collaborative Filtering (Koren).- Item Recommendation from Implicit Feedback (Rendle).- Deep Learning for Recommender Systems (Zhang).- Context Aware Re commender Sytems : From Foundatiom to Recent Developments (Bauman).- Semantics and Content-based Recommendations (Musto).- Part 2: Special Recommendation Techniques.- Session-based Recommender Systems (lannoch)..- Adversarial Recommender Systems: Attack,Defense, and Advances (Di Nola).- Group Recommender Systems: Beyond Preferance Aggregation (Masthoff).- People-to-People Reciprocal Recommenders (Koprinska).- Natural Language Processing for Recommender Systems (Sar-Shalom).- Design and Evaluation of Cross-domain Recommender Systems (Cremonesi).- Part 3: Value and Impact of Recommender Systems.- Value and Impact of Recommender SyTable of ContentsPreface.- Introduction.- Part 1: General Recommendation Techniques.- Trust Your Neighbors: A Comprehensive Survey of Neighborhood-based Methods for Recommender Systems (Desrosiers).- Advances in Collaborative Filtering (Koren).- Item Recommendation from Implicit Feedback (Rendle).- Deep Learning for Recommender Systems (Zhang).- Context Aware Re commender Sytems : From Foundatiom to Recent Developments (Bauman).- Semantics and Content-based Recommendations (Musto).- Part 2: Special Recommendation Techniques.- Session-based Recommender Systems (lannoch)..- Adversarial Recommender Systems: Attack,Defense, and Advances (Di Nola).- Group Recommender Systems: Beyond Preferance Aggregation (Masthoff).- People-to-People Reciprocal Recommenders (Koprinska).- Natural Language Processing for Recommender Systems (Sar-Shalom).- Design and Evaluation of Cross-domain Recommender Systems (Cremonesi).- Part 3: Value and Impact of Recommender Systems.- Value and Impact of Recommender Systems (Zanker).- Evaluating Recommender Systems (Shani).- Novelty and Diversity in Recommender Systems (Castells).- Multistakeholder Recommender Systems (Burke).- Fairness in Recommender Systems (Ekstrand).- Part 4: Human Computer Interaction.- Beyond Explaining Single Item Recommendations (Tintarev).- Personality and Recommender Systems (Tkalčič).- Individual and Group Decision Making and Recommender Systems (Jameson).- Part 5: Recommender Systems Applications .- Social Recommender Systems (Guy).- Food Recommender Systems (Trattner).- Music Recommendation Systems: Techniques, Use Cases, and Challenges (Schedl).- Multimedia Recommender Systems: Algorithms and Challenges (Deldjoo).- Fashion Recommender Systems (Dokoohaki).

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Book SynopsisDr Michael Negnevitsky is a Professor in Electrical Engineering and Computer Science at the University of Tasmania, Australia. The book has developed from his lectures to undergraduates. Educated as an electrical engineer, Dr Negnevitsky's many interests include artificial intelligence and soft computing. His research involves the development and application of intelligent systems in electrical engineering, process control and environmental engineering. He has authored and co-authored over 300 research publications including numerous journal articles, four patents for inventions and two books.Trade Review“This book covers many areas related to my module. I would be happy to recommend this book to my students. I believe my students would be able to follow this book without any difficulty. Book chapters are very well organised and this will help me to pick and choose the subjects related to this module.” Dr Ahmad Lotfi, Nottingham Trent University, UKTable of Contents Contents Preface xii New to this edition xiii Overview of the book xiv Acknowledgements xvii 1 Introduction to knowledge-based intelligent systems 1 1.1 Intelligent machines, or what machines can do 1 1.2 The history of artificial intelligence, or from the ‘Dark Ages’ to knowledge-based systems 4 1.3 Summary 17 Questions for review 21<

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Book SynopsisManaging and Mining Graph Data is a comprehensive survey book in graph management and mining. It contains extensive surveys on a variety of important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy.Trade ReviewFrom the reviews:“This book provides a survey of some recent advances in graph mining. It contains chapters on graph languages, indexing, clustering, pattern mining, keyword search, and pattern matching. … The book is targeted at advanced undergraduate or graduate students, faculty members, and researchers from both industry and academia. … I highly recommend this book to someone who is starting to explore the field of graph mining or wants to delve deeper into this exciting field.” (Dimitrios Katsaros, ACM Computing Reviews, December, 2010)Table of ContentsAn Introduction to Graph Data.- Graph Data Management and Mining: A Survey of Algorithms and Applications.- Graph Mining: Laws and Generators.- Query Language and Access Methods for Graph Databases.- Graph Indexing.- Graph Reachability Queries: A Survey.- Exact and Inexact Graph Matching: Methodology and Applications.- A Survey of Algorithms for Keyword Search on Graph Data.- A Survey of Clustering Algorithms for Graph Data.- A Survey of Algorithms for Dense Subgraph Discovery.- Graph Classification.- Mining Graph Patterns.- A Survey on Streaming Algorithms for Massive Graphs.- A Survey of Privacy-Preservation of Graphs and Social Networks.- A Survey of Graph Mining for Web Applications.- Graph Mining Applications to Social Network Analysis.- Software-Bug Localization with Graph Mining.- A Survey of Graph Mining Techniques for Biological Datasets.- Trends in Chemical Graph Data Mining.

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Book SynopsisThis work presents a data visualization technique that combines graph-based topology representation and dimensionality reduction methods to visualize the intrinsic data structure in a low-dimensional vector space.Table of ContentsVector Quantisation and Topology-Based Graph RepresentationGraph-Based Clustering AlgorithmsGraph-Based Visualisation of High-Dimensional Data

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Book SynopsisProfessor Judea Pearl won the 2011 Turing Award for fundamental contributions to artificial intelligence through the development of a calculus for probabilistic and causal reasoning. This book contains the original articles that led to the award, as well as other seminal works, divided into four parts: heuristic search, probabilistic reasoning, causality, first period (1988-2001), and causality, recent period (2002-2020). Each of these parts starts with an introduction written by Judea Pearl. The volume also contains original, contributed articles by leading researchers that analyze, extend, or assess the influence of Pearl''s work in different fields: from AI, Machine Learning, and Statistics to Cognitive Science, Philosophy, and the Social Sciences. The first part of the volume includes a biography, a transcript of his Turing Award Lecture, two interviews, and a selected bibliography annotated by him.

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Book Synopsis.NET Remoting.- Absolute Time.- Abstract Versus Concrete Temporal Query Languages.- Abstraction.- Access Control.- Access Control Administration Policies.- Access Control Policy Languages.- Access Path.- ACID Properties.- Active and Real-Time Data Warehousing.- Active Database Coupling Modes.- Active Database Execution Model.- Active Database Knowledge Model.- Active Database Management System Architecture.- Active Database Rulebase.- Active Database, Active Database (Management) System.- Active Storage.- Active XML.- Activity.- Activity Diagrams.- Actors/Agents/Roles.- Adaptive Interfaces.- Adaptive Middleware for Message Queuing Systems.- Adaptive Query Processing.- Adaptive Stream Processing.- ADBMS.- Administration Model for RBAC.- Administration Wizards.- Advanced Information Retrieval Measures.- Aggregation: Expressiveness and Containment.- Aggregation-Based Structured Text Retrieval.- Air Indexes for Spatial Databases.- AJAX.- Allen's Relations.- AMOSQL.- AMS Sketch.- Anchor TexTable of Contents.NET Remoting.- Absolute Time.- Abstract Versus Concrete Temporal Query Languages.- Abstraction.- Access Control.- Access Control Administration Policies.- Access Control Policy Languages.- Access Path.- ACID Properties.- Active and Real-Time Data Warehousing.- Active Database Coupling Modes.- Active Database Execution Model.- Active Database Knowledge Model.- Active Database Management System Architecture.- Active Database Rulebase.- Active Database, Active Database (Management) System.- Active Storage.- Active XML.- Activity.- Activity Diagrams.- Actors/Agents/Roles.- Adaptive Interfaces.- Adaptive Middleware for Message Queuing Systems.- Adaptive Query Processing.- Adaptive Stream Processing.- ADBMS.- Administration Model for RBAC.- Administration Wizards.- Advanced Information Retrieval Measures.- Aggregation: Expressiveness and Containment.- Aggregation-Based Structured Text Retrieval.- Air Indexes for Spatial Databases.- AJAX.- Allen's Relations.- AMOSQL.- AMS Sketch.- Anchor Text.- Annotation.- Annotation-based Image Retrieval.- Anomaly Detection on Streams.- Anonymity.- ANSI/INCITS RBAC Standard.- Answering Queries Using Views.- Anti-monotone Constraints.- Applicability Period.- Application Benchmark.- Application Recovery.- Application Server.- Application-Level Tuning.- Applications of Emerging Patterns for Microarray Gene Expression Data Analysis.- Applications of Sensor Network Data Management.- Approximate Queries in Peer-to-Peer Systems.- Approximate Query Processing.- Approximate Reasoning.- Approximation of Frequent Itemsets.- Apriori Property and Breadth-First Search Algorithms.- Architecture-Conscious Database System.- Archiving Experimental Data.- Armstrong Axioms.- Array Databases.- Array Databases_old.- Association Rule Mining on Streams.- Association Rules.- Asymmetric Encryption.- Atelic Data.- Atomic Event.- Atomicity.- Audio.- Audio Classification.- Audio Content Analysis.- Audio Metadata.- Audio Representation.- Audio Segmentation.- Auditing and Forensic Analysis.- Authentication.- Automatic Image Annotation.- Autonomous Replication.- Average Precision.- Average Precision at n.- Average Precision Histogram.- Average R-Precision.- B+-Tree.- Backup and Restore.- Bag Semantics.- Bagging.- Bayesian Classification.- Benchmark Frameworks.- Benchmarks for Big Data Analytics.- Big Data Platforms for Data Analytics.- Big Stream Systems.- Biological Metadata Management.- Biological Networks.- Biological Resource Discovery.- Biological Sequences.- Biomedical Data/Content Acquisition, Curation.- Biomedical Image Data Types and Processing.- Biomedical Scientific Textual Data Types and Processing.- Biostatistics and Data Analysis.- Bi-Temporal Indexing.- Bitemporal Interval.- Bitemporal Relation.- Bitmap Index.- Bitmap-based Index Structures.- Blind Signatures.- Bloom Filters.- BM25.- Boolean Model.- Boosting.- Bootstrap.- Boyce-Codd Normal Form.- BP-Completeness.- Bpref.- Browsing.- Browsing in Digital Libraries.- B-Tree Locking.- Buffer Management.- Buffer Manager.- Buffer Pool.- Business Intelligence.- Business Process Execution Language.- Business Process Management.- Business Process Modeling Notation.- Business Process Reengineering.- Cache-Conscious Query Processing.- Calendar.- Calendric System.- CAP Theorem.- Cardinal Direction Relationships.- Cartesian Product.- Cataloging in Digital Libraries.- Causal Consistency.- Certain (and Possible) Answers.- Change Detection on Streams.- Channel-Based Publish/Subscribe.- Chart.- Chase.- Checksum and Cyclic Redundancy Check Mechanism.- Choreography.- Chronon.- Citation.- Classification.- Classification by Association Rule Analysis.- Classification in Streams.- Client-Server Architecture.- Clinical Data Acquisition, Storage and Management.- Clinical Data and Information Models.- Clinical Data Quality and Validation.- Clinical Decision Support.- Clinical Document Architecture.- Clinical Event.- Clinical Knowledge Repository.- Clinical Observation.- Clinical Ontologies.- Clinical Order.- Closed Itemset Mining and Non-redundant Association Rule Mining.- Closest-Pair Query.- Cloud Computing.- Cloud Intelligence.- Cluster and Distance Measure.- Clustering for Post Hoc Information Retrieval.- Clustering on Streams.- Clustering Overview and Applications.- Clustering Validity.- Clustering with Constraints.- Collaborative Filtering.- Column Segmentation.- Column Stores.- Common Warehouse Metamodel.- Comparative Visualization.- Compensating Transactions.- Complex Event.- Complex Event Processing.- Composed Services and WS-BPEL.- Composite Event.- Composition.- Comprehensions.- Compression of Mobile Location Data.- Computational Media Aesthetics.- Computationally Complete Relational Query Languages.- Computerized Physician Order Entry.- Conceptual Modeling Foundations.- Conceptual Schema Design.- Concurrency Control - Traditional Approaches.- Concurrency Control for Replicated Databases.- Concurrency Control Manager.- Conditional Tables.- Conjunctive Query.- Connection.- Consistency Models For Replicated Data.- Consistent Query Answering.- Constraint Databases.- Constraint Query Languages.- Constraint-Driven Database Repair.- Content-and-Structure Query.- Content-Based Publish/Subscribe.- Content-Based Video Retrieval.- Content-Only Query.- Context.- Contextualization in Structured Text Retrieval.- Continuous Data Protection.- Continuous Monitoring of Spatial Queries.- Continuous Multimedia Data Retrieval.- Continuous Queries in Sensor Networks.- Continuous Query.- ConTract.- Control Data.- Convertible Constraints.- Coordination.- Copyright Issues in Databases.- CORBA.- Correctness Criteria Beyond Serializability.- Cost and quality trade-offs in crowdsourcing.- Cost Estimation.- Count-Min Sketch.- Coupling and De-coupling.- Covering Index.- Crash Recovery.- Cross-Language Mining and Retrieval.- Cross-Modal Multimedia Information Retrieval.- Cross-Validation.- Crowd Database Operators.- Crowd Database Systems.- Crowd Mining and Analysis.- Crowdsourcing Geographic Information Systems.- Cube.- Cube Implementations.- Current Semantics.- Curse of Dimensionality.- Daplex.- Data Acquisition and Dissemination in Sensor Networks.- Data Aggregation in Sensor Networks.- Data Broadcasting, Caching and Replication in Mobile Computing.- Data Cleaning.- Data Compression in Sensor Networks.- Data Conflicts.- Data Definition.- Data Definition Language (DDL).- Data Dictionary.- Data Encryption.- Data Estimation in Sensor Networks.- Data Exchange.- Data Fusion.- Data Fusion in Sensor Networks.- Data Generation.- Data Governance.- Data Integration Architectures and Methodology for the Life Sciences.- Data Integration in Web Data Extraction System.- Data Management for VANETs.- Data Management Fundamentals: Database Management System.- Data Management in Data Centers.- Data Manipulation.- Data Manipulation Language (DML).- Data Mart.- Data Migration Management.- Data Mining.- Data Partitioning.- Data Privacy and Patient Consent.- Data Profiling.- Data Provenance.- Data Quality Assessment.- Data Quality Dimensions.- Data Quality Models.- Data Rank/Swapping.- Data Reduction.- Data Replication.- Data Sampling.- Data Scrubbing.- Data Sketch/Synopsis.- Data Skew.- Data Storage and Indexing in Sensor Networks.- Data Stream.- Data Stream Management Architectures and Prototypes.- Data Types in Scientific Data Management.- Data Uncertainty Management in Sensor Networks.- Data Visualization.- Data Warehouse.- Data Warehouse Life-Cycle and Design.- Data Warehouse Maintenance, Evolution and Versioning.- Data Warehouse Metadata.- Data Warehouse Security.- Data Warehousing for Clinical Research.- Data Warehousing in Cloud Environments.- Data Warehousing on Non-Conventional Data.- Data Warehousing Systems: Foundations and Architectures.- Data, Text, and Web Mining in Healthcare.- Database.- Database Adapter and Connector.- Database Administrator (DBA).- Database Appliances.- Database Benchmarks.- Database Clustering Methods.- Database Clusters.- Database Dependencies.- Database Design.- Database Languages for Sensor Networks.- Database Machine.- Database Management System.- Database Middleware.- Database Repair.- Database Reverse Engineering.- Database Schema.- Database Security.- Database System.- Database Techniques to Improve Scientific Simulations.- Database Trigger.- Database Tuning using Combinatorial Search.- Database Tuning using Online Algorithms.- Database Tuning using Trade-off Elimination.- Database Use in Science Applications.- Datalog.- DBMS Component.- DBMS Interface.- DCE.- DCOM.- Decay Models.- Decision Rule Mining in Rough Set Theory.- Decision Tree Classification.- Decision Trees.- Declarative Networking.- Deductive Data Mining using Granular Computing.- Deduplication.- Deduplication in Data Cleaning.- Deep Instantiation.- Deep-Web Search.- Dense Index.- Dense Pixel Displays.- Density-based Clustering.- Description Logics.- Design for Data Quality.- Dewey Decimal System.- Diagram.- Difference.- Differential Privacy.- Digital Archives and Preservation.- Digital Curation.- Digital Elevation Models.- Digital Libraries.- Digital Rights Management.- Digital Signatures.- Dimension.- Dimension Reduction Techniques for Clustering.- Dimensionality Reduction.- Dimensionality Reduction Techniques For Nearest Neighbor Computations.- Dimension-Extended Topological Relationships.- Direct Attached Storage.- Direct Manipulation.- Disaster Recovery.- Disclosure Risk.- Discounted Cumulated Gain.- Discovery.- Discrete Wavelet Transform and Wavelet Synopses.- Discretionary Access Control.- Disk.- Disk Power Saving.- Distortion Techniques.- Distributed Architecture.- Distributed Concurrency Control.- Distributed Data Streams.- Distributed Database Design.- Distributed Database Systems.- Distributed DBMS.- Distributed Deadlock Management.- Distributed File Systems.- Distributed Hash Table.- Distributed Join.- Distributed Machine Learning.- Distributed Query Optimization.- Distributed Query Processing.- Distributed Recovery.- Distributed Spatial Databases.- Distributed Transaction Management.- Divergence from Randomness Models.- D-measure.- Document.- Document Clustering.- Document Databases.- Document Field.- Document Length Normalization.- Document Links and Hyperlinks.- Document Representations (Inclusive Native and Relational).- Dublin Core.- Dynamic Graphics.- Dynamic Web Pages.- eAccessibility.- ECA Rule Action.- ECA Rule Condition.- ECA Rules.- e-Commerce Transactions.- Effectiveness Involving Multiple Queries.- Ehrenfeucht-Fraïssé Games.- Elasticity.- Electronic Dictionary.- Electronic Encyclopedia.- Electronic Health Record.- Electronic Ink Indexing.- Electronic Newspapers.- Eleven Point Precision-recall Curve.- Emergent Semantics.- Emerging Pattern Based Classification.- Emerging Patterns.- Energy Efficiency in Data Centers.- Ensemble.- Enterprise Application Integration.- Enterprise Content Management.- Enterprise Service Bus.- Enterprise Terminology Services.- Entity Relationship Model.- Entity Resolution.- Entity Retrieval.- Equality-Generating Dependencies.- ERR- Expected Reciprocal Rank.- ERR-IA Intent-aware ERR.- Escrow Transactions.- European Law in Databases.- Evaluation Metrics for Structured Text Retrieval.- Evaluation of Relational Operators.- Event.- Event and Pattern Detection over Streams.- Event Causality.- Event Channel.- Event Cloud.- Event Detection.- Event Driven Architecture.- Event Flow.- Event in Active Databases.- Event in Temporal Databases.- Event Lineage.- Event Pattern Detection.- Event Prediction.- Event Processing Agent.- Event Processing Network.- Event Sink.- Event Source.- Event Specification.- Event Stream.- Event Transformation.- Event-Driven Business Process Management.- Eventual Consistency.- Evidence Based Medicine.- Executable Knowledge.- Execution Skew.- Explicit Event.- Exploratory Data Analysis.- Expressive Power of Query Languages.- Extended Entity-Relationship Model.- Extended Transaction Models and the ACTA Framework.- Extendible Hashing.- Extraction, Transformation, and Loading.- Faceted Search.- Fault-Tolerance and High Availability in Data Stream Management Systems.- Feature Extraction for Content-Based Image Retrieval.- Feature Selection for Clustering.- Feature-Based 3D Object Retrieval.- Field-Based Information Retrieval Models.- Field-Based Spatial Modeling.- First-Order Logic: Semantics.- First-Order Logic: Syntax.- Fixed Time Span.- Flex Transactions.- FM Synopsis.- F-Measure.- Focused Web Crawling.- FOL Modeling of Integrity Constraints (Dependencies).- Forever.- Form.- Fourth Normal Form.- FQL.- Fractal.- Frequency Moments.- Frequent Graph Patterns.- Frequent Items on Streams.- Frequent Itemset Mining with Constraints.- Frequent Itemsets and Association Rules.- Frequent Partial Orders.- Fully-Automatic Web Data Extraction.- Functional Data Model.- Functional Dependencies for Semi-Structured Data.- Functional Dependency.- Functional Query Language.- Fuzzy Models.- Fuzzy Relation.- Fuzzy Set.- Fuzzy Set Approach.- Fuzzy/Linguistic IF-THEN Rules and Linguistic Descriptions.- Gazetteers.- Gene Expression Arrays.- Generalization of ACID Properties.- Generalized Search Tree.- Genetic Algorithms.- Geographic Information System.- Geographical Information Retrieval.- Geography Markup Language.- Geometric Stream Mining.- GEO-RBAC Model.- Georeferencing.- Geosocial Networks.- Geospatial Metadata.- Geo-Targeted Web Search.- GMAP.- Grammar Inference.- Graph.- Graph Data Management in Scientific Applications.- Graph Database.- Graph Management in the Life Sciences.- Graph Mining.- Graph Mining on Streams.- Graph OLAP.- Graphical Models for Uncertain Data Management.- Grid and Workflows.- Grid File (and Family).- GUIs for Web Data Extraction.- Hash Functions.- Hash Join.- Hash-based Indexing.- Healthcare Metrics.- Hierarchial Clustering.- Hierarchical Data Model.- Hierarchical Data Summarization.- Hierarchical Heavy Hitter Mining on Streams.- Hierarchy.- High Dimensional Indexing.- Histogram.- Histograms on Streams.- History in Temporal Databases.- Homomorphic Encryption.- Horizontally Partitioned Data.- Human Factors Modeling in Crowdsourcing.- Human-centered Computing: Application to Multimedia.- Human-Computer Interaction.- Hypertexts.- I/O Model of Computation.- Icon.- Iconic Displays.- Image.- Image Content Modeling.- Image Database.- Image Management for Biological Data.- Image Metadata.- Image Querying.- Image Representation.- Image Retrieval and Relevance Feedback.- Image Segmentation.- Image Similarity.- Implementation of Database Operators (Joins, Group by, etc.).- Implication of Constraints.- Implications of Genomics for Clinical Informatics.- Implicit Event.- Incomplete Information.- Inconsistent Databases.- Incremental Computation of Queries.- Incremental Crawling.- Incremental Maintenance of Views with Aggregates.- Index Creation and File Structures.- Index Join.- Index Structures for Biological Sequences.- Index Tuning.- Indexed Sequential Access Method.- Indexing and Similarity Search.- Indexing Compressed Text.- Indexing Historical Spatio-Temporal Data.- Indexing in pub/sub systems.- Indexing Metric Spaces.- Indexing of Data Warehouses.- Indexing of the Current and Near-Future Positions of Moving Objects.- Indexing Techniques for Multimedia Data Retrieval.- Indexing the Web.- Indexing Uncertain Data.- Indexing Units of Structured Text Retrieval.- Indexing with Crowds.- Individually Identifiable Data.- Inference Control in Statistical Databases.- Information Extraction.- Information Filtering.- Information Foraging.- Information Integration.- Information Integration Techniques for Scientific Data.- Information Lifecycle Management.- Information Loss Measures.- Information Navigation.- Information Quality.- Information Quality and Decision Making.- Information Quality Assessment.- Information Quality Policy and Strategy.- Information Quality: Managing Information as a Product.- Information Retrieval.- Information Retrieval Models.- Information Retrieval Operations.- Infrastructure As-A-Service (IaaS).- Initiative for the Evaluation of XML Retrieval.- Initiator.- In-Network Query Processing.- Integrated DB and IR Approaches.- Integration of Rules and Ontologies.- Intelligent Storage Systems.- Interactive Analytics in Social Media.- Interface.- Interface Engines in Healthcare.- Interoperability in Data Warehouses.- Interoperation of NLP-based Systems with Clinical Databases.- Inter-Operator Parallelism.- Inter-Query Parallelism.- Intra-operator Parallelism.- Intra-Query Parallelism.- Intrusion Detection Technology.- Inverse Document Frequency.- Inverted Files.- IP Storage.- Iterator.- Java Database Connectivity.- Java Enterprise Edition.- Java Metadata Facility.- Join.- Join Dependency.- Join Index.- Join Order.- k-Anonymity.- Karp-Luby Sampling.- KDD Pipeline.- Key.- K-Means and K-Medoids.- Knowledge Base.- Knowledge Base Extraction.- Language Models.- Languages for Web Data Extraction.- Learning Distance Measures.- Lexical Analysis of Textual Data.- Licensing and Contracting Issues in Databases.- Lifespan.- Lightweight Ontologies.- Linear Hashing.- Linear Regression.- Linked Open Data.- Linking and Brushing.- Load Balancing in Peer-to-Peer Overlay Networks.- Load Shedding.- LOC METS.- Locality.- Locality of Queries.- Location Based Recommendation.- Location Management in Mobile Environments.- Location Update Management.- Location-Based Services.- Locking Granularity and Lock Types.- Logging and Recovery.- Logging/Recovery Subsystem.- Logical and Physical Data Independence.- Logical Database Design: from Conceptual to Logical Schema.- Logical Document Structure.- Logical Foundations of Web Data Extraction.- Logical Models of Information Retrieval.- Logical Unit Number.- Logical Unit Number Mapping.- Logical Volume Manager.- Log-Linear Regression.- Loop.- Loose Coupling.- Machine Learning in Computational Biology.- Main Memory.- Main Memory DBMS.- Maintenance of Materialized Views with Outer-Joins.- Maintenance of Recursive Views.- Managing Compressed Structured Text.- Managing Data Integration Uncertainty.- Managing Probabilistic Entity Extraction.- Mandatory Access Control.- MANET Databases.- MAP.- Map Matching.- MapReduce.- Markup Language.- MashUp.- Massive Array of Idle Disks.- Matrix Masking.- Max-Pattern Mining.- Mean Reciprocal Rank.- Measure.- Mediation.- Membership Query.- Memory Hierarchy.- Memory Locality.- Merkle Trees.- Message Authentication Codes.- Message Queuing Systems.- Meta Data Repository.- Meta Object Facility.- Metadata.- Metadata Interchange Specification.- Metadata Registry, ISO/IEC 11179.- Metamodel.- Metasearch Engines.- Metric Space.- Microaggregation.- Microbenchmark.- Microdata.- Microdata Rounding.- Middleware Support for Database Replication and Caching.- Middleware Support for Precise Failure Semantics.- Mining of Chemical Data.- Mobile Database.- Mobile Interfaces.- Mobile resource search.- Mobile Sensor Network Data Management.- Model Management.- Model-based Querying in Sensor Networks.- Monotone Constraints.- Monte Carlo Methods for Uncertain Data.- Moving Object.- Moving Objects Databases and Tracking.- MRR.- Multi-Data Center Consistency Properties.- Multi-Data Center Replication Protocols.- Multidimensional Data Formats.- Multidimensional Modeling.- Multidimensional Scaling.- Multi-Level Modeling.- Multi-Level Recovery and the ARIES Algorithm.- Multilevel Secure Database Management System.- Multilevel Transactions and Object-Model Transactions.- Multimedia Data.- Multimedia Data Buffering.- Multimedia Data Indexing.- Multimedia Data Querying.- Multimedia Data Storage.- Multimedia Databases.- Multimedia Information Retrieval Model.- Multimedia Metadata.- Multimedia Presentation Databases.- Multimedia Resource Scheduling.- Multimedia Retrieval Evaluation.- Multimedia Tagging.- Multimodal Interfaces.- Multi-Pathing.- Multiple Representation Modeling.- Multi-Query Optimization.- Multi-Resolution Terrain Modeling.- Multi-Step Query Processing.- Multitenancy.- Multi-Tier Architecture.- Multi-tier Storage Systems.- Multivalued Dependency.- Multivariate Visualization Methods.- Multi-version Serializability and Concurrency Control.- Naive Tables.- Narrowed Extended XPath I.- Natural Interaction.- Near-duplicate Retrieval.- Nearest Neighbor Classification.- Nearest Neighbor Query.- Nearest Neighbor Query in Spatio-temporal Databases.- Nested Loop Join.- Nested Transaction Models.- Network Attached Secure Device.- Network Attached Storage.- Network Data Model.- Neural Networks.- N-Gram Models.- Noise Addition.- Nonparametric Data Reduction Techniques.- Non-Perturbative Masking Methods.- Non-relational Streams.- Nonsequenced Semantics.- Normal Form ORA-SS Schema Diagrams.- Normal Forms and Normalization.- NoSQL Stores.- Now in Temporal Databases.- Null Values.- OASIS.- Object Constraint Language.- Object Data Models.- Object Identity.- Object Recognition.- Object Relationship Attribute Data Model for Semi-structured Data.- Object Storage Protocol.- Object-Role Modeling.- OLAM.- OLAP Personalization and Recommendation.- OLAP Personalization and Recommendation_old.- One-Copy-Serializability.- One-Pass Algorithm.- On-Line Analytical Processing.- Online Recovery in Parallel Database Systems.- Ontologies and Life Science Data Management.- Ontology.- Ontology Elicitation.- Ontology Engineering.- Ontology Visual Querying.- Ontology-Based Data Access and Integration.- Open Database Connectivity.- Open Information Extraction.- Open Nested Transaction Models.- Operator-Level Parallelism.- Opinion Mining.- Optimistic Replication and Resolution.- Optimization and Tuning in Data Warehouses.- OQL.- Orchestration.- Order Dependency.- OR-Join.- OR-Split.- OSQL.- Outlier Detection.- Overlay Network.- OWL: Web Ontology Language.- P/FDM.- Parallel and Distributed Data Warehouses.- Parallel Coordinates.- Parallel Data Placement.- Parallel Database Management.- Parallel Hash Join, Parallel Merge Join, Parallel Nested Loops Join.- Parallel Query Execution Algorithms.- Parallel Query Optimization.- Parallel Query Processing.- Parameterized Complexity of Queries.- Parametric Data Reduction Techniques.- Partial Replication.- Path Query.- Pattern-Growth Methods.- Peer Data Management System.- Peer to Peer Overlay Networks: Structure, Routing and Maintenance.- Peer-To-Peer Content Distribution.- Peer-to-Peer Data Integration.- Peer-to-Peer Publish-Subscribe Systems.- Peer-to-Peer Storage.- Peer-to-Peer System.- Peer-to-Peer Web Search.- Performance Analysis of Transaction Processing Systems.- Performance Monitoring Tools.- Period-Stamped Temporal Models.- Personalized Web Search.- Petri Nets.- Physical Clock.- Physical Database Design for Relational Databases.- Physical Layer Tuning.- Pipeline.- Pipelining.- Platform As-A-Service (PaaS).- Point-in-Time Copy.- Point-Stamped Temporal Models.- Polytransactions.- Positive Relational Algebra.- Possible Answers.- PRAM.- Precision.- Precision and Recall.- Precision at n.- Precision-Oriented Effectiveness Measures.- Predictive Analytics.- Preference Queries.- Preference Specification.- Prescriptive Analytics.- Presenting Structured Text Retrieval Results.- Primary Index.- Principal Component Analysis.- Privacy.- Privacy Metrics.- Privacy Policies and Preferences.- Privacy through Accountability.- Privacy-Enhancing Technologies.- Privacy-Preserving Data Mining.- Privacy-Preserving DBMSs.- Private Information Retrieval.- Probabilistic Databases.- Probabilistic Entity Resolution.- Probabilistic Retrieval Models and Binary Independence Retrieval (BIR) Model.- Probabilistic Skylines.- Probabilistic Spatial Queries.- Probabilistic Temporal Databases.- Probability Ranking Principle.- Probability Smoothing.- Process Life Cycle.- Process Mining.- Process Modeling.- Process Optimization.- Process Structure of a DBMS.- Processing Overlaps in Structured Text Retrieval.- Processing Structural Constraints.- Processor Cache.- Profiles and Context for Structured Text Retrieval.- Projection.- Propagation-based Structured Text Retrieval.- Protection from Insider Threats.- Provenance.- Provenance and Reproducibility.- Provenance in Databases.- Provenance in Scientific Databases.- Provenance in Workflows.- Provenance Management.- Provenance Standards.- Provenance Storage.- Provenance: Privacy and Security.- Pseudonymity.- Publish/Subscribe.- Publish/Subscribe over Streams.- Punctuations.- Q-measure.- Quadtrees (and Family).- Qualitative Temporal Reasoning.- Quality and Trust of Information Content and Credentialing.- Quality of Data Warehouses.- Quantiles on Streams.- Quantitative Association Rules.- QUEL.- Query by Humming.- Query Containment.- Query Evaluation Techniques for Multidimensional Data.- Query Expansion for Information Retrieval.- Query Expansion Models.- Query Language.- Query Languages and Evaluation Techniques for Biological Sequence Data.- Query Languages for the Life Sciences.- Query Load Balancing in Parallel Database Systems.- Query Optimization.- Query Optimization (in Relational Databases).- Query Optimization in Sensor Networks.- Query Plan.- Query Point Movement Techniques for Content-Based Image Retrieval.- Query Processing.- Query Processing (in Relational Databases).- Query Processing and Optimization in Object Relational Databases.- Query Processing in data integration systems.- Query Processing in Data Warehouses.- Query Processing in Deductive Databases.- Query Processing over Uncertain Data.- Query Processor.- Query Rewriting.- Query Rewriting Using Views.- Query Translation.- Quorum Systems.- Randomization Methods to Ensure Data Privacy.- Range Query.- Rank-aware Query Processing.- Ranked XML Processing.- Ranking Functions.- Ranking Views.- Rank-Join.- Rank-Join Indices.- Raster Data Management and Multi-Dimensional Arrays.- RDF Stores.- RDF Technology.- Real and Synthetic Test Datasets.- Real-Time Transaction Processing.- Recall.- Receiver Operating Characteristic.- Recommender Systems.- Record Linkage.- Record Matching.- Redundant Arrays of Independent Disks.- Reference Knowledge.- Region Algebra.- Regulatory Compliance in Data Management.- Relational Algebra.- Relational Calculus.- Relational Model.- Relationships in Structured Text Retrieval.- Relative Time.- Relevance.- Relevance Feedback.- Relevance Feedback for Content-Based Information Retrieval.- Relevance Feedback for Text Retrieval.- Replica Control.- Replica Freshness.- Replicated Data Types.- Replicated Database Concurrency Control.- Replication.- Replication Based on Group Communication.- Replication for Availability and Fault-Tolerance.- Replication for High Availability.- Replication for Paxos.- Replication for Scalability.- Replication in Multi-Tier Architectures.- Replication with Snapshot Isolation.- Reputation and Trust.- Request Broker.- Residuated Lattice.- Resource Allocation Problems in Spatial Databases.- Resource Description Framework.- Resource Description Framework (RDF) Schema (RDFS).- Resource Identifier.- Result Display.- Retrospective Event Processing.- Reverse Nearest Neighbor Query.- Reverse Top-k Queries.- Rewriting Queries using Views.- RMI.- Road Networks.- Rocchio's Formula.- Role Based Access Control.- R-Precision.- R-Tree (and Family).- Rule-based Classification.- Safety and Domain Independence.- Sagas.- Sampling Techniques for Statistical Databases.- SAN File System.- Scalable Decision Tree Construction.- Scheduler.- Scheduling Strategies for Data Stream Processing.- Schema Evolution.- Schema Mapping.- Schema Mapping Composition.- Schema Matching.- Schema Tuning.- Schema Versioning.- Scheme/Ontology Extraction.- Scientific Databases.- Scientific Visualization.- Scientific Workflows.- Score Aggregation.- Screen Scraper.- SCSI Target.- SDC Score.- Search Engine Metrics.- Searching Digital Libraries.- Second Normal Form (2NF).- Secondary Index.- Secure Data Outsourcing.- Secure Database Development.- Secure Multiparty Computation Methods.- Secure Transaction Processing.- Security Services.- Segmentation and Stratification.- Segmentation and Stratification_old.- Selection.- Selectivity Estimation.- Self-Maintenance of Views.- Self-Management Technology in Databases.- Semantic Atomicity.- Semantic Crowd Sourcing.- Semantic Data Integration for Life Science Entities.- Semantic Data Model.- Semantic Matching.- Semantic Modeling and Knowledge Representation for Multimedia Data.- Semantic Modeling for Geographic Information Systems.- Semantic Overlay Networks.- Semantic Social Web.- Semantic Streams.- Semantic Web.- Semantic Web Query Languages.- Semantic Web Services.- Semantics-based Concurrency Control.- Semijoin.- Semijoin Program.- Semi-Structured Data.- Semi-Structured Data Model.- Semi-Structured Database Design.- Semi-Structured Query Languages.- Semi-Supervised Learning.- Sensor Networks.- Sequenced Semantics.- Sequential Patterns.- Serializability.- Serializable Snapshot Isolation.- Service Component Architecture (SCA).- Service Oriented Architecture.- Session.- Shared-Disk Architecture.- Shared-Memory Architecture.- Shared-Nothing Architecture.- Side-Effect-Free View Updates.- Signature Files.- Similarity and Ranking Operations.- Simplicial Complex.- Singular Value Decomposition.- Skyline Queries and Pareto Optimality.- Snapshot Equivalence.- Snapshot Isolation.- Snippet.- Snowflake Schema.- SOAP.- Social Applications.- Social influence.- Social Media Analysis.- Social Media Analytics.- Social Media Harvesting.- Social network analysis.- Social Networks.- Software As-A-Service (SaaS).- Software Transactional Memory.- Software-Defined Storage.- Solid State Drive (SSD).- Sort-Merge Join.- Space-Filling Curves.- Space-Filling Curves for Query Processing.- SPARQL.- Sparse Index.- Spatial and Spatio-Temporal Data Models and Languages.- Spatial and Temporal Data Warehouses .- Spatial Anonymity.- Spatial Data Analysis.- Spatial Data Mining.- Spatial Data Types.- Spatial Datawarehousing.- Spatial Indexing Techniques.- Spatial Join.- Spatial Keyword Search.- Spatial Matching Problems.- Spatial Network Databases.- Spatial Operations and Map Operations.- Spatial Queries in the Cloud.- Spatio-Temporal Data Mining.- Spatio-Temporal Data Types.- Spatio-Temporal Data Warehouses.- Spatiotemporal Interpolation Algorithms.- Spatio-Temporal Selectivity Estimation.- Spatio-Temporal Trajectories.- Specialization and Generalization.- Specificity.- Spectral Clustering.- Split.- Split Transactions.- SQL.- SQL Analytics on Big Data.- SQL Isolation Levels.- SQL-Based Temporal Query Languages.- Stable Distribution.- Stack-based Query Language.- Staged DBMS.- Standard Effectiveness Measures.- Star Index.- Star Schema.- State-based Publish/Subscribe.- Statistical Data Management.- Statistical Disclosure Limitation For Data Access.- Steganography.- Stemming.- Stop-&-go Operator.- Stoplists.- Storage Access Models.- Storage Area Network.- Storage Consolidation.- Storage Devices.- Storage Grid.- Storage Management.- Storage Management Initiative-Specification.- Storage Manager.- Storage Network Architectures.- Storage Networking Industry Association.- Storage of Large Scale Multidimensional Data.- Storage Power Management.- Storage Protection.- Storage Protocols.- Storage Resource Management.- Storage Security.- Storage Virtualization.- Stored Procedure.- Stream Mining.- Stream Models.- Stream Processing.- Stream processing on modern hardware.- Stream Reasoning.- Stream Sampling.- Stream Similarity Mining.- Streaming Analytics.- Streaming Applications.- Stream-Oriented Query Languages and Operators.- Strong Consistency Models for Replicated Data.- Structural Indexing.- Structure Analytics in Social Media.- Structure Weight.- Structured Data in Peer-to-Peer Systems.- Structured Document Retrieval.- Structured Text Retrieval Models.- Subject Spaces.- Subspace Clustering Techniques.- Success at n.- Succinct Constraints.- Suffix Tree.- Summarizability.- Summarization.- Support Vector Machine.- Supporting Transaction Time Databases.- Symbolic Representation.- Symmetric Encryption.- Synopsis Structure.- Synthetic Microdata.- System R (R*) Optimizer.- Table.- Tabular Data.- Taxonomy: Biomedical Health Informatics.- tBench.- Telic Distinction in Temporal Databases.- Telos.- Temporal Access Control.- Temporal Aggregation.- Temporal Algebras.- Temporal Analytics in Social Media.- Temporal Benchmarks.- Temporal Coalescing.- Temporal Compatibility.- Temporal Conceptual Models.- Temporal Constraints.- Temporal Data Mining.- Temporal Data Models.- Temporal Database.- Temporal Datawarehousing.- Temporal Dependencies.- Temporal Element.- Temporal Expression.- Temporal Generalization.- Temporal Granularity.- Temporal Homogeneity.- Temporal Indeterminacy.- Temporal Integrity Constraints.- Temporal Joins.- Temporal Logic in Database Query Languages.- Temporal Logical Models.- Temporal Object-Oriented Databases.- Temporal Periodicity.- Temporal Projection.- Temporal PSM.- Temporal Query Languages.- Temporal Query Processing.- Temporal Relational Calculus.- Temporal Specialization.- Temporal Strata.- Temporal Support in the SQL Standard.- Temporal Vacuuming.- Temporal Visual Languages.- Temporal XML.- Term Proximity.- Term Statistics for Structured Text Retrieval.- Term Weighting.- Test Collection.- Text Analytics.- Text Analytics in Social Media.- Text Categorization.- Text Clustering.- Text Compression.- Text Generation.- Text Index Compression.- Text Indexing and Retrieval.- Text Indexing Techniques.- Text Mining.- Text Mining of Biological Resources.- Text Representation.- Text Segmentation.- Text Semantic Representation.- Text Stream Processing.- Text Streaming Model.- Text Summarization.- Text Visualization.- TF*IDF.- Thematic Map.- Third Normal Form.- Three-Dimensional GIS and Geological Applications.- Three-Phase Commit.- Tight Coupling.- Time Aggregated Graphs.- Time and Information Retrieval.- Time Domain.- Time in Philosophical Logic.- Time Instant.- Time Interval.- Time Period.- Time Series Query.- Time Span.- Time-Line Clock.- Timeslice Operator.- Topic Detection and Tracking.- Topic Maps.- Topic-based Publish/Subscribe.- Top-k Queries.- Top-K Selection Queries on Multimedia Datasets.- Topological Data Models.- Topological Relationships.- Trajectory.- Transaction.- Transaction Chopping.- Transaction Management.- Transaction Manager.- Transaction Models - the Read/Write Approach.- Transaction Time.- Transactional Middleware.- Transactional Processes.- Transactional Stream Processing.- Transaction-Time Indexing.- Tree-based Indexing.- Treemaps.- Triangular Norms.- Triangulated Irregular Network.- Trie.- Trip Planning Queries.- Trust and Reputation in Peer-to-Peer Systems.- Trust in Blogosphere.- Trusted Hardware.- TSQL2.- Tuning Concurrency Control.- Tuple-Generating Dependencies.- Two-Dimensional Shape Retrieval.- Two-Phase Commit.- Two-Phase Commit Protocol.- Two-Phase Locking.- Two-Poisson model.- Type-based Publish/Subscribe.- U-measure.- Uncertain Data Lineage.- Uncertain Data Mining.- Uncertain Data Models.- Uncertain Data Streams.- Uncertain Data Summarization.- Uncertain Graph Data Management.- Uncertain Spatial Data Management.- Uncertain Top-k Queries.- Uncertainty in Events.- Uncertainty Management in Scientific Database Systems.- Unicode.- Unified Modeling Language.- Union.- Unobservability.- Updates and Transactions in Peer-to-Peer Systems.- Updates through Views.- Usability.- User-Defined Time.- Valid Time.- Valid-Time Indexing.- Value Equivalence.- Variable Time Span.- Vector-Space Model.- Vertically Partitioned Data.- Video.- Video Content Analysis.- Video Content Modeling.- Video Content Structure.- Video Metadata.- Video Querying.- Video Representation.- Video Scene and Event Detection.- Video Segmentation.- Video Sequence Indexing.- Video Shot Detection.- Video Summarization.- View Adaptation.- View Definition.- View Maintenance.- View Maintenance Aspects.- View-based Data Integration.- Views.- Virtual Partitioning.- Visual Analytics.- Visual Association Rules.- Visual Classification.- Visual Clustering.- Visual Content Analysis.- Visual Data Mining.- Visual Formalisms.- Visual Interaction.- Visual Interfaces.- Visual Interfaces for Geographic Data.- Visual interfaces for streaming data.- Visual Metaphor.- Visual On-Line Analytical Processing (OLAP).- Visual Perception.- Visual Query Language.- Visual Representation.- Visualization for Information Retrieval.- Visualization Pipeline.- Visualizing Categorical Data.- Visualizing Clustering Results.- Visualizing Hierarchical Data.- Visualizing Network Data.- Visualizing Quantitative Data.- Volume.- Voronoi Diagrams.- W3C.- WAN Data Replication.- Wavelets on Streams.- Weak Consistency Models for Replicated Data.- Weak Equivalence.- Web 2.0/3.0.- Web Advertising.- Web Characteristics and Evolution.- Web Crawler Architecture.- Web Data Extraction System.- Web ETL.- Web Harvesting.- Web Information Extraction.- WEB Information Retrieval Models.- Web Mashups.- Web Page Quality Metrics.- Web Question Answering.- Web Search Query Rewriting.- Web Search Relevance Feedback.- Web Search Relevance Ranking.- Web Search Result Caching and Prefetching.- Web Search Result De-duplication and Clustering.- Web Services.- Web Services and the Semantic Web for Life Science Data.- Web Spam Detection.- Web Transactions.- Web Views.- What-If Analysis.- WIMP Interfaces.- Window operator in RDBMS.- Window-based Query Processing.- Windows.- Workflow Constructs.- Workflow Evolution.- Workflow Join.- Workflow Management.- Workflow Management and Workflow Management System.- Workflow Management Coalition.- Workflow Model.- Workflow Model Analysis.- Workflow Patterns.- Workflow Schema.- Workflow Transactions.- Wrapper Induction.- Wrapper Maintenance.- Wrapper Stability.- Write Once Read Many.- XML.- XML Access Control.- XML Attribute.- XML Benchmarks.- XML Compression.- XML Document.- XML Element.- XML Indexing.- XML Information Integration.- XML Integrity Constraints.- XML Metadata Interchange.- XML Metadata Interchange Specification (XMI).- XML Parsing, SAX/DOM.- XML Process Definition Language.- XML Programming.- XML Publish/Subscribe.- XML Publishing.- XML Retrieval.- XML Schema.- XML Selectivity Estimation.- XML Storage.- XML Stream Processing.- XML Tree Pattern, XML Twig Query.- XML Tuple Algebra.- XML Typechecking.- XML Types.- XML Updates.- XML Views.- XPath/XQuery.- XQuery Full-Text.- XQuery Processors.- XSL/XSLT.- Zero-One Laws.- Zooming Techniques.- α-nDCG.-

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Book SynopsisIn recent years, tremendous research has been devoted to the design of database systems for real-time applications, called real-time database systems (RTDBS), where transactions are associated with deadlines on their completion times, and some of the data objects in the database are associated with temporal constraints on their validity.Table of ContentsList of Figures. List of Tables. Acknowledgments. Preface. Contributing Authors. I: Overview, Misconceptions and Issues. 1. Real-Time Database Systems: An Overview of System Characteristics and Issues; Tei-Wei Kuo, Kam-Yiu Lam. 2. Misconceptions About Real-Time Databases; J.A. Stankovic, et al. 3. Applications and System Characteristics; D. Locke. II: Real-Time Concurrency Control. 4. Conservative and Optimistic Protocols; Tei-Wei Kuo, Kam-Yiu Lam. 5. Semantics-Based Concurrency Control; Tei-Wei Kuo. 6. Real-Time Index Concurrency Control; J.R. Haritsa, S. Seshadri. III: Run-Time System Management. 7. Buffer Management in Real-Time Active Database Systems; A. Datta, S. Mukherjee. 8. Disk Scheduling; Ben Kao, R. Cheng. 9. System Failure and Recovery; R.M. Sivasankaran, et al. 10. Overload Management in RTDBs; J. Hansson, S.H. Son. 11. Secure Real-Time Transaction Processing; J.R. Haritsa, B. George. IV: Active Issues and Triggering. 12. System Framework of ARTDBs; J. Hansson, S.F. Andler. 13. Reactive Mechanisms; J. Mellin, et al. 14. Updates and View Maintenance; Ben Kao, et al. V: Distributed Real-Time Database Systems. 15. Distributed Concurrency Control; Ö. Ulusoy. 16. Data Replication and Availability; Ö. Ulusoy. 17. Real-Time Commit Processing; J.R. Haritsa, et al. 18. Mobile Distributed Real-Time Database Systems; Kam-Yiu Liam, Tei-Wei Kuo.VI: Prototypes and Future Directions. 19. Prototypes: Programmed Stock Trading; B. Adelberg, Ben Kao. 20. Future Directions; Tei-Wei Kuo, Kam-Yiu Lam. Index.

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Book SynopsisCollect and analyze sensor and usage data from Internet of Things applications with Microsoft Azure IoT Suite. Internet connectivity to everyday devices such as light bulbs, thermostats, and even voice-command devices such as Google Home and Amazon.com''s Alexa is exploding. These connected devices and their respective applications generate large amounts of data that can be mined to enhance user-friendliness and make predictions about what a user might be likely to do next. Microsoft''s Azure IoT Suite is a cloud-based platform that is ideal for collecting data from connected devices. You''ll learn in this book about data acquisition and analysis, including real-time analysis. Real-world examples are provided to teach you to detect anomalous patterns in your data that might lead to business advantage. We live in a time when the amount of data being generated and stored is growing at an exponential rate. Understanding and getting real-time insight into these datTable of ContentsIntroductionPart I: Getting Started1. The World of Big Data and IoT2. Generating Data with DevicesPart II: Data on the Move3. Azure IoT Hub4. Ingesting Data with Azure IoT Hub5. Azure Stream Analytics6. Real-Time Data Streaming7. Azure Data Factory8. Integrating Data Between Data Stores Using Azure Data FactoryPart III: Data at Rest9. Azure Data Lake Store10. Azure Data Lake Analytics11. U-SQL12. Azure HDInsight13. Real-time Insights and Reporting on Big Data14. Azure Machine LearningPart IV: More on Cortana Intelligence15. Azure Data Catalog16. Azure Event Hubs

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Book SynopsisLearn how to build a data science technology stack and perform good data science with repeatable methods. You will learn how to turn data lakes into business assets.The data science technology stack demonstrated in Practical Data Science is built from components in general use in the industry. Data scientist Andreas Vermeulen demonstrates in detail how to build and provision a technology stack to yield repeatable results. He shows you how to apply practical methods to extract actionable business knowledge from data lakes consisting of data from a polyglot of data types and dimensions. What You''ll Learn Become fluent in the essential concepts and terminology of data science and data engineering  Build and use a technology stack that meets industry criteria Master the methods for retrieving actionable business knowledge Coordinate the handling ofTable of Contents

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Book SynopsisTable of ContentsPart 1: Data Structures.- Chapter 1: Intro to DSA, Types and Big-O.- Chapter 2: Linear Data Structures.- Chapter 3: Tree Data Structures.- Chapter 4: Hash Data Structures.- Chapter 5: Graphs.- Part 2: Algorithms.- Chapter 6: Linear and Binary Search.- Chapter 7: Sorting Algorithms.- Chapter 8: Searching Algorithms.- Chapter 9: Clustering Algorithms.- Chapter 10: Randomness.- Chapter 11: Scheduling Algorithms.- Chapter 12: Algorithm Planning and Design.- Appendix A: Going Further.-

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Book Synopsis Childhood learning is now more screen-based than ever before, and app developers are flocking in droves to this lucrative and exciting market. The younger generation deserves the best, and growing up in a digital world has made them discerning and demanding customers. Creating a valuable user experience for a child is as complex and involved as when designing a typical app for an adult, if not more, and Designing Digital Products for Kids is here to be your guide. Author and designer Rubens Cantuni recognizes the societal importance of a high-quality and ethical app experience for children. There is room for significant improvement in this space, and Cantuni helps you optimize it. Designing Digital Products for Kids walks hopeful developers through digital product design-including research, concept, design, release, marketing, testing, analyzing, and iterating-all while aiming to build specifically for children. Industry experts and their reTable of Contents 1. Why Design Apps for Kids? 2. Before You Start, Know the Industry 3. Know Your Target Audience 4. Concept 5. Gamification 6. Safety Measures. 7. Interaction Design 8. UI Design 9. User Testing with Kids 10. Market Your Product 11. Beyond the Screen 12. Conclusion

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Book SynopsisDesign for large-scale, high-performance queries using Snowflake's query processing engine to empower data consumers with timely, comprehensive, and secure access to data. This book also helps you protect your most valuable data assets using built-in security features such as end-to-end encryption for data at rest and in transit. It demonstrates key features in Snowflake and shows how to exploit those features to deliver a personalized experience to your customers. It also shows how to ingest the high volumes of both structured and unstructured data that are needed for game-changing business intelligence analysis.Mastering Snowflake Solutionsstarts with a refresher on Snowflake's unique architecture before getting into the advanced concepts that make Snowflake the market-leading product it is today. Progressing through each chapter, you will learn how to leverage storage, query processing, cloning, data sharing, and continuous data protection features. This approach allows for greater Table of Contents1. Snowflake Architecture2. Data Movement3. Cloning4. Managing Security and User Access Control 5. Protecting Data in Snowflake6. Business Continuity and Disaster Recovery7. Data Sharing and the Data Cloud8. Programming9. Advanced Performance Tuning10. Developing Applications in Snowflake

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Book SynopsisImplement the Snowflake Data Cloud using best practices and reap the benefits of scalability and low-cost from the industry-leading, cloud-based, data warehousing platform. This book provides a detailed how-to explanation, and assumes familiarity with Snowflake core concepts and principles. It is a project-oriented book with a hands-on approach to designing, developing, and implementing your Data Cloud with security at the center. As you work through the examples, you will develop the skill, knowledge, and expertise to expand your capability by incorporating additional Snowflake features, tools, and techniques. Your Snowflake Data Cloud will be fit for purpose, extensible, and at the forefront of both Direct Share, Data Exchange, and Snowflake Marketplace. Building the Snowflake Data Cloud helps you transform your organization into monetizing the value locked up within your data. As the digital economy takes hold, with data volume, velociTable of ContentsPart I. Context 1. The Snowflake Data Cloud 2. Breaking Data Siloes Part II. Concepts 3. Architecture 4. Account Security5. Role Based Access Control (RBAC)6. Account Usage StorePart III. Tools7. Ingesting Data8. Data Pipelines9. Data Presentation10. Semi Structured and Unstructured DataPart IV. Management11. Query Optimizer Basics12. Data Management13. Data Modelling14. Snowflake Data Cloud By Example

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Book SynopsisThis book will show how generative technology works and the drivers. It will also look at the applications showing what various startupsand large companies are doing in the space.There will also be a look at the challenges and risk factors. During the past decade, companies have spent billions on AI. But the focus has been on applying the technology to predictions which is known as analytical AI. It can mean that you receive TikTok videos that you cannot resist. Or analytical AI can fend against spam or fraud or forecast when a package will be delivered. While such things are beneficial, there is much more to AI. The next megatrend will be leveraging the technology to be creative. For example, you could take a book and an AI model will turn it into a movie at very little cost. This is all part of generative AI. It's still in the nascent stages but it is progressing quickly. Generative AI can already create engaging blog posts, social media messages, beautiful artwork and compellinTable of ContentsChapter 1: Introduction to Generative AI.- Chapter 2: Data.- Chapter 3: AI Fundamentals.- Chapter 4: Core Generative AI Technology.- Chapter 5: Large Language Models.- Chapter 6: Auto Code Generation.- Chapter 7: The Transformation of Business.- Chapter 8: The Impact on Major Businesses.- Chapter 9: The Future.

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Book SynopsisIntermediate-Advanced user levelTable of ContentsChapter 1: Getting Ready.- Chapter 2: Working with Table Design.- Chapter 3: Table Relationships and Working With Joins.- Chapter 4: Working with Calculated Data.- Chapter 5: Aggregating Data.- Chapter 6: Creating and Using Views and Friends.- Chapter 7: Working With Subqueries and Common Table Expressions.- Chapter 8: Working With Window Functions.-Chapter 9: More on Common Table Expressions.- Chapter 10: More Techniques with SQL: Triggers, Pivot Tables, and Variables.- Appendix A.

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Book SynopsisWhile neuroscientists garner success in identifying brain regions and in analyzing individual neurons, ground is still being broken at the intermediate scale of understanding how neurons combine to encode information. This book proposes a method of representing information in a computer that would be suited for modelling the brain's methods of processing information. Holographic reduced representations (HRRs) are introduced here to model how the brain distributes each piece of information among thousands of neurons. It has been previously thought that the grammatical structure of a language cannot be encoded practically in a distributed representation, but HRRs can overcome the problems of earlier proposals. Thus this work has implications for psychology, neuroscience, linguistics, computer science and engineering.

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Book SynopsisLearn how AI-assisted coding using ChatGPT and GitHub Copilot can dramatically increase your productivity (and fun) in writing regular expressions and other programmes. "How these tools can be both so very amazing in what they produce, and simultaneously so utterly doltish in their numerous failures, is the main thing this book tries to understand. For reasons I attempt to elucidate throughout, of all the domains of computer programming, games with regular expressions are particularly well suited for getting a grasp on the peculiar behaviors of AI." From the Preface For programmers of any experience level – no experience with AI coding tools is required. Regular Expression Puzzles and AI Coding Assistants is the story of two competitors. On the one side is David Mertz, an expert programmer and the author of the Web's most popular Regex tutorial. On the other are the AI powerhouse coding assistants, GitHub Copilot and OpenAI ChatGPT. Here's how the contest works: David invents 24 Regex problems he calls puzzles and shows you how to tackle each one. When he's done he has Copilot and ChatGPT work the same puzzles. What they produce intrigues him. Which side is likelier to get it right? Which will write simple and elegant code? Which one makes the smartest use of lesser-known Regex library features? Read the book to find out. David also offers AI best practices, showing how smart prompts return better results. By the end, you'll be a master at solving your own Regex puzzles, whether you use AI or not. About the technology Ground-breaking large language model research from OpenAI, Google, Amazon, and others, have transformed expectations of machine-generated software. But how do these AI assistants, like ChatGPT and GitHub Copilot, measure up against regular expressions—a workhorse technology for developers used to describe, find, and manipulate patterns in the text? Regular expressions are compact, complex, and subtle. Will AI assistants handle the challenge?Trade Review"AI coding assistants are here, and they're transforming how programming is and will be done. If you know regular expressions, pick up this book and learn all about AI coding assistants. If you don't know regular expressions, well, pick it up anyway and experience how you learn with AI coding assistants." Dr. Daniel Zingaro University of Toronto, author of Algorithmic Thinking "I am a strong believer in Augmented Intelligence and welcome tools such as ChatGPT & Co-Pilot. To take meaningful steps toward Augmented Intelligence, we will need to understand both Humans' and Machines' strengths and weaknesses. This is where David's book comes in. Through the puzzles, David was able to show it well. Strongly recommended for anyone who is looking to use Artificial Intelligence for their business, to tap into its strength and prepare for potential risks." Koo Ping Shung, Data Science Rex

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