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• 1. Data: An Organisational Asset.-
• 2. A History of Databases.-
• 3. Physical Storage and Distribution.-
• 4. Relational Databases.-
• 5. NoSQL Databases.-
• 6. Big Data.-
• 7. Object and Object-Relational Databases.-
• 8. In-Memory Databases.-
• 9. Hierarchical Databases.-
• 10. Distributed Databases.-
• 11. Graph Databases.-
• 12. Database Scalability.-
• 13. Database Availability.-
• 14. Database Performance.-
• 15. Security.
• (source: Nielsen Book Data)

Modern businesses depend on data for their very survival, creating a need for sophisticated databases and database technologies to help store, organise and transport their valuable data. This updated and expanded, easy-to-read textbook/reference presents a comprehensive introduction to databases, opening with a concise history of databases and of data as an organisational asset. As relational database management systems are no longer the only database solution, the book takes a wider view of database technology, encompassing big data, NoSQL, object and object-relational, and in-memory databases. Presenting both theoretical and practical elements, the new edition also examines the issues of scalability, availability, performance and security encountered when building and running a database in the real world. Topics and features: Presents review and discussion questions at the end of each chapter, in addition to skill-building, hands-on exercises Provides new material on database adaptiveness, integration, and efficiency in relation to data growth Introduces a range of commercial databases and encourages the reader to experiment with these in an associated learning environment Reviews use of a variety of databases in business environments, including numerous examples Discusses areas for further research within this fast-moving domain With its learning-by-doing approach, supported by both theoretical and practical examples, this clearly-structured textbook will be of great value to advanced undergraduate and postgraduate students of computer science, software engineering, and information technology. Practising database professionals and application developers will also find the book an ideal reference that addresses today's business needs.
(source: Nielsen Book Data)

• Part I: Databases in Context
• Data: An Organisational Asset
• The History of Databases
• Physical Storage and Distribution
• Part II: Database Types
• Relational: The Start of the Modern Era in Databases
• NoSQL: Column-Based and Document-Based Databases
• Big Data
• Object and Object-Relational
• In-Memory Databases
• Part III: What Database Professionals Worry About
• Scalability
• Availability
• Performance
• Security.

Modern businesses depend on data for their very survival, creating a need for sophisticated databases and database technologies to help store, organise and transport their valuable data. This easy-to-read textbook/reference presents a comprehensive introduction to databases, opening with a concise history of databases and of data as an organisational asset. As relational database management systems are no longer the only database solution, the book takes a wider view of database technology, encompassing big data, NoSQL, object and object-relational, and in-memory databases. The text also examines the issues of scalability, availability, performance and security encountered when building and running a database in the real world. Topics and features: Presents review and discussion questions at the end of each chapter, in addition to skill-building, hands-on exercises Introduces the fundamental concepts and technologies in database systems, placing these in an historic context Describes the challenges faced by database professionals Reviews the use of a variety of database types in business environments Discusses areas for further research within this fast-moving domain Suggests a structure for a potential university course in the preface With its learning-by-doing approach, supported by both theoretical and practical examples, this clearly-structured textbook will be of great value to advanced undergraduate and postgraduate students of computer science, software engineering, and information technology. Practising database professionals and application developers will also find the book an ideal reference that addresses today's business needs.

• Introduction
• PostgreSQL
• Riak
• HBase
• MongoDB
• CouchDB
• Neo4J
• Redis
• Wrapping up
• Appendixes. Database overview tables
• The CAP theorem.

Redis, Neo4J, Couch, Mongo, HBase, Riak and Postgres. With each database, you'll tackle a real-world data problem that highlights the concepts and features that make it shine. You'll explore the five data models employed by these databases-relational, key/value, columnar, document and graph-and which kinds of problems are best suited to each. You'll learn how MongoDB and CouchDB-both JavaScript powered, document oriented datastores-are strikingly different. Learn about the Dynamo heritage at the heart of Riak and Cassandra. Understand MapReduce and how to use it to solve Big Data problems. Build clusters of servers using scalable services like Amazon's Elastic Compute Cloud (EC2). Discover the CAP theorem and its implications for your distributed data. Understand the tradeoffs between consistency and availability, and when you can use them to your advantage. Use multiple databases in concert to create a platform that's more than the sum of its parts, or find one that meets all your needs at once. Seven Databases in Seven Weeks will give you a broad understanding of the databases, their strengths and weaknesses, and how to choose the ones that fit your needs. What You Need: To get the most of of this book you'll have to follow along, and that means you'll need a *nix shell (Mac OSX or Linux preferred, Windows users will need Cygwin), and Java 6 (or greater) and Ruby 1.8.7 (or greater). Each chapter will list the downloads required for that database.
(source: Nielsen Book Data)

• Machine Learning.- An Approach Based on Bayesian Networks for Query Selectivity Estimation.- An Exploration of Cross-Modal Retrieval for Unseen Concepts.- Continuous Patient-centric Sequence Generation via Sequentially Coupled Adversarial Learning.- DMMAM: A Deep Multi-source Multi-task Attention Model for Intensive Care Unit Diagnosis.- Learning k-Occurrence Regular Expressions with Interleaving.- Learning from User Social Relation for Document Sentiment Classification.- Reinforcement Learning to Diversify Recommendations.- Retweeting Prediction using Matrix Factorization with Binomial Distribution and Contextual Information.- Sparse Gradient Compression for Distributed SGD.- STDR: A Deep Learning Method for Travel Time Estimation.- Using Fractional Latent Topic to Enhance Recurrent Neural Network in Text Similarity Modeling.- Efficiently Mining Maximal Diverse Frequent Itemsets.- Privacy and Graph.- Efficient Local Search for Minimum Dominating Sets in Large Graphs.- Multi-level Graph Compression for Fast Reachability Detection.- Multiple Privacy Regimes Mechanism For Local Differential Privacy.- Privacy Preserving Elastic Stream Processing with Clouds using Homomorphic Encryption.- Select the Best for Me: Privacy-preserving Polynomial Evaluation Algorithm over Road Network.- Recommendation.- AdaCML: Adaptive Collaborative Metric Learning for Recommendation.- Adaptive Attention-Aware Gated Recurrent Unit for Sequential Recommendation.- Attention and Convolution Enhanced Memory Network for Sequential Recommendation.- Attention-based Neural Tag Recommender System.- Density Matrix based Preference Evolution Networks for E-commerce Recommendation.- Multi-Source Multi-Net Micro-Video Recommendation with Hidden Item Category Discovery.- Incorporating Task-oriented Representation in Text Classification.- Music Playlist Recommendation with Long Short-Term Memory.- Online Collaborative Filtering with Implicit Feedback.- Subspace Ensemble-based Neighbor User Searching for Neighborhood-based Collaborative Filtering.- Towards Both Local and Global Query Result Diversification.- Social Network.- Structured Spectral Clustering of PurTree Data.- Dynamic stochastic block model with scale-free characteristic for temporal complex networks.- In Good Company: Efficient Retrieval of the Top-k Most Relevant Event-Partner Pairs.- Local Experts Finding across Multiple Social Networks.- SBRNE: An Improved Unified Framework for Social and Behavior Recommendations with Network Embedding.- User Intention-based Document Summarization on Heterogeneous Sentence Networks.- Spatial.- A Hierarchical Index Structure for Region-aware Spatial Keyword Search with Edit Distance Constraint.- Collective POI Querying Based on Multiple Keywords and User Preference.- DPSCAN: Structural Graph Clustering Based on Density Peaks.- Efficient Processing of Spatial Group Preference Queries.- Reverse-Auction-Based Competitive Order Assignment for Mobile Taxi-Hailing Systems.- Top-k Spatio-Topic Query on Social Media Data.- Spatio-temporal.- A Frequency-aware Spatio-Temporal Network for Traffic Flow Prediction.- Efficient Algorithms for Solving Aggregate Keyword Routing Problems.- Perceiving Topic Bubbles: Local Topic Detection in Spatio-temporal Tweet Stream.- Real-Time Route Planning and Online Order Dispatch for Bus-Booking Platforms.- STL: Online Detection of Taxi Trajectory Anomaly based on Spatial-Temporal Laws.
• (source: Nielsen Book Data)

This two-volume set LNCS 11446 and LNCS 11447 constitutes the refereed proceedings of the 24th International Conference on Database Systems for Advanced Applications, DASFAA 2019, held in Chiang Mai, Thailand, in April 2019. The 92 full papers and 64 short papers were carefully selected from a total of 501 submissions. In addition, 13 demo papers and 6 tutorial papers are included. The full papers are organized in the following topics: big data; clustering and classification; crowdsourcing; data integration; embedding; graphs; knowledge graph; machine learning; privacy and graph; recommendation; social network; spatial; and spatio-temporal. The short papers, demo papers, and tutorial papers can be found in the volume LNCS 11448, which also includes the workshops of DASFAA 2019.
(source: Nielsen Book Data)

This two volume set LNCS 6587 and LNCS 6588 constitutes the refereed proceedings of the 16th International Conference on Database Systems for Advanced Applications, DASFAA 2011, held in Saarbrucken, Germany, in April 2010. The 53 revised full papers and 12 revised short papers presented together with 2 invited keynote papers, 22 demonstration papers, 4 industrial papers, 8 demo papers, and the abstract of 1 panel discussion, were carefully reviewed and selected from a total of 225 submissions. The topics covered are social network, social network and privacy, data mining, probability and uncertainty, stream processing, graph, XML, XML and graph, similarity, searching and digital preservation, spatial queries, query processing, as well as indexing and high performance.
(source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 21st British National Conference on Databases, BNCOD 2004, held in Edinburgh, Scotland, UK in July 2004.The 21 revised full papers presented together with an invited paper and the abstract of an invited talk were carefully reviewed and selected from more than 70 submissions. The papers are organized in topical sections on data streams, integration and heterogeneity, data analytics and manipulation, XML, interfaces and visualization, spatial data, and TLAD workshop papers.
(source: Nielsen Book Data)

• Data Management Technology Kairometer: The Historical Context Foreword Preface Introduction PART I 2014 ACM A.M. TURING AWARD PAPER AND LECTURE The Land Sharks Are on the Squawk Box PART II MIKE STONEBRAKER'S CAREER 1. Make it Happen: The Life of Michael Stonebraker PART III MIKE STONEBRAKER SPEAKS OUT: AN INTERVIEW WITH MARIANNE WINSLETT 2. Mike Stonebraker Speaks Out: An Interview PART IV THE BIG PICTURE 3. Leadership and Advocacy 4. Perspectives: The 2014 ACM Turing Award 5. Birth of an Industry: Path to the Turing Award 6. A Perspective of Mike from a 50-Year Vantage Point PART V STARTUPS 7. How to Start a Company in Five (Not So) Easy Steps 8. How to Create and Run a Stonebraker Startup
• The Real Story 9. Getting Grownups in the Room: A VC Perspective PART VI DATABASE SYSTEMS RESEARCH 10. Where Good Ideas Come From and How to Exploit Them 11. Where We Have Failed 12. Stonebraker and Open Source 13. The Relational Database Management Systems Genealogy PART VII CONTRIBUTIONS BY SYSTEM 14. Research Contributions of Mike Stonebraker: An Overview PART VII.A RESEARCH CONTRIBUTIONS BY SYSTEM 15. The Later Ingres Years 16. Looking Back at Postgres 17. Databases Meet the Stream Processing Era 18. C-Store: Through the Eyes of a Ph.D. Student 19. In-Memory, Horizontal, and Transactional: The H-Store OLTP DBMS Project 20. Scaling Mountains: SciDB and Scientific Data Management 21. Data Unification at Scale: Data Tamer 22. The BigDAWG Polystore System 23. Data Civilizer: End-to-End Support for Data Discovery, Integration, and Cleaning PART VII.B CONTRIBUTIONS FROM BUILDING SYSTEMS 24. The Commercial Ingres Codeline 25. The Postgres and Illustra Codelines 26. The Aurora/Borealis/SteamBase Codelines: A Tale of Three Systems 27. The Vertica Codeline 28. The VoltDB Codeline 29. The SciDB Codeline: Crossing the Chasm 30. The Tamr Codeline 31. The BigDAWG Codeline PART VIII PERSPECTIVES 32. IBM Relational Database Code Bases 33. Aurum: A Story about Research Taste 34. Nice: Or What It Was Like to Be Mike's Student 35. Michael Stonebraker: Competitor, Collaborator, Friend 36. The Changing of the Database Guard PART IX SEMINAL WORKS OF MICHAEL STONEBRAKER AND HIS COLLABORATORS OTLP Through the Looking Glass, and What We Found There ""One Size Fits All"": An Idea Whose Time Has Come and Gone The End of an Architectural Era (It's Time for a Complete Rewrite) C-Store: A Column-Oriented DBMS The Implementation of POSTGRES The Design and Implementation of INGRES The Collected Works of Michael Stonebraker References Index Biographies.
• (source: Nielsen Book Data)

This book celebrates Michael Stonebraker's accomplishments that led to his 2014 ACM A.M. Turing Award ""for fundamental contributions to the concepts and practices underlying modern database systems."" The book describes, for the broad computing community, the unique nature, significance, and impact of Mike's achievements in advancing modern database systems over more than forty years. Today, data is considered the world's most valuable resource, whether it is in the tens of millions of databases used to manage the world's businesses and governments, in the billions of databases in our smartphones and watches, or residing elsewhere, as yet unmanaged, awaiting the elusive next generation of database systems. Every one of the millions or billions of databases includes features that are celebrated by the 2014 Turing Award and are described in this book. Why should I care about databases? What is a database? What is data management? What is a database management system (DBMS)? These are just some of the questions that this book answers, in describing the development of data management through the achievements of Mike Stonebraker and his over 200 collaborators. In reading the stories in this book, you will discover core data management concepts that were developed over the two greatest eras (so far) of data management technology. The book is a collection of 36 stories written by Mike and 38 of his collaborators: 23 world-leading database researchers, 11 world-class systems engineers, and 4 business partners. If you are an aspiring researcher, engineer, or entrepreneur you might read these stories to find these turning points as practice to tilt at your own computer-science windmills, to spur yourself to your next step of innovation and achievement.
(source: Nielsen Book Data)

• CALIDAD DE DATOS; PÁGINA LEGAL; ÍNDICE; PRÓLOGO; PREFACIO; 1 FUNDAMENTOS DE LA CALIDAD DE DATOS; 1.1 CALIDAD DE DATOS; 1.2 DATO, INFORMACIÓN, CONOCIMIENTO; 1.3 CICLO DE VIDA; 1.4 CAUSAS DE PROBLEMAS DERIVADOS DE NIVELES; 1.5 GOBIERNO VS. GESTIÓN DE DATOS; 1.6 LECTURAS RECOMENDADAS; 1.7 SITIOS WEB; 2 DATOS MAESTROS; 2.1 INTRODUCCIÓN A LOS DATOS; 2.2 GESTIÓN DE DATOS; 2.3 RESOLUCIÓN DE ENTIDADES; 2.4 ARQUITECTURA; 2.5 PRINCIPIOS DE LA ARQUITECTURA; 2.6 MODELO DE MADUREZ; 2.7 ESTÁNDARES PARA DATOS; 2.8 SOLUCIONES COMERCIALES; 2.9 LECTURAS RECOMENDADAS; 2.10 SITIOS WEB

• 3 CALIDAD DE PROCESOS DE DATOS3.1 DAMA DMBOK: DATA MANAGEMENT; 3.2 MODELO; 3.3 DATA MANAGEMENT; 3.4 MODELO DE IBM; 3.5 MODELO DE GARTNER DE GESTIÓN; 3.6 TQDM; 3.8 MODELO MAMD; 3.9 LECTURAS RECOMENDADAS; 3.10 SITIOS WEB; 4 CALIDAD DE REPOSITORIOS; 4.1 MODELO DE CALIDAD; 4.2 MEDIDAS DE CALIDAD; 4.3 PROCESO DE EVALUACIÓN; 4.4 CERTIFICACIÓN DE LA CALIDAD; 4.5 EJEMPLOS DE CERTIFICACIÓN; 4.6 LECTURAS RECOMENDADAS; 4.7 SITIOS WEB; 5 MONETIZACIÓN DE LOS DATOS; 5.1 INTRODUCCIÓN; 5.2 CARACTERÍSTICAS ESPECIALES; 5.3 APROXIMACIONES A LA VALORACIÓN; 5.4 VALOR DE LOS; 5.5 INFONOMÍA

• 5.6 LECTURAS RECOMENDADASANEXO I; ACRÓNIMOS; BIBLIOGRAFÍA