Databases / Data management Books

Book SynopsisThere is an urgent need to develop and integrate new statistical, mathematical, visualization, and computational models with the ability to analyze Big Data in order to retrieve useful information to aid clinicians in accurately diagnosing and treating patients. The main focus of this book is to review and summarize state-of-the-art big data and deep learning approaches to analyze and integrate multiple data types for the creation of a decision matrix to aid clinicians in the early diagnosis and identification of high risk patients for human diseases and disorders. Leading researchers will contribute original research book chapters analyzing efforts to solve these important problems.Table of ContentsBig Data Applications in Lung Research. Artificial convolution neural network techniques and applications for big data of lung for nodule detection. Deep learning with non-medical training used for pathology identification in big data chest images. Unsupervised pre-training across image domains improves lung tissue classification in lung big data sets. Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks in big data sets of CT Lungs. Big Data Applications in Colon Research. A comprehensive computer-aided polyp detection system for big data colonoscopy videos. Automatic polyp detection in big data colonoscopy videos using an ensemble of convolutional neural networks. A new 2.5 D representation for lymph node detection using random sets of deep convolutional neural network observations in big data colonoscopy. Off-the-shelf convolutional neural network features for pulmonary nodule detection in big data computed tomography scans. Big Data Applications in Breast Cancer. Mitosis detection in big data breast cancer histology images with deep neural networks. Convolutional neural networks for mass lesion classification in big data mammography. Standard plane localization in fetal ultrasound via domain transferred deep neural networks in large ultrasound data sets. Unregistered multiview analysis with pre-trained deep learning models in large mammographic data sets. Big Data Applications in Brain Imaging. Brain tumor segmentation with deep neural networks using big data sets. Deep convolutional neural networks for multi-modality isointense infant brain image segmentation in big data MRI images. Deep neural networks segment neuronal membranes in electron microscopy images. Alzheimer's Disease Diagnosis by Adaptation of 3D Convolutional Network in large MRI brain images. Computer-aided pulmonary embolism detection using a novel vessel-aligned multi-planar image representation and convolutional neural networks. Big Data Applications in Heart Imaging. Automating carotid intima-media thickness video interpretation with convolutional neural networks. Interleaved text/image deep mining on a very large-scale radiology database. Fine-tuned convolutional neural nets for cardiac MRI acquisition plane recognition in big data sets. Left ventricle segmentation from cardiac MRI combining level set methods with deep belief networks in large MRI populations. Big Data Applications in Urology and Abdomen Imaging. A New NMF-Autoencoder Based CAD System for Early Diagnosis of Prostate Cancer by considering big data sets. Image-Based Computer-Aided Diagnosis for Early Diagnosis of Prostate Cancer in large data sets. Deep convolutional networks for pancreas segmentation in large scale CT imaging. A Promising Non-invasive CAD System for Kidney Function Assessment.

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Book SynopsisFully updated and expanded from the previous edition, A Practical Guide to Database Design, Second Edition is intended for those involved in the design or development of a database system or application. It begins by illustrating how to develop a Third Normal Form data model where data is placed “where it belongs”. The reader is taken step-by-step through the Normalization process, first using a simple then a more complex set of data requirements. Next, usage analysis for each Logical Data Model is reviewed and a Physical Data Model is produced that will satisfy user performance requirements. Finally, each Physical Data Model is used as input to create databases using both Microsoft Access and SQL Server.The book next shows how to use an industry-leading data modeling tool to define and manage logical and physical data models, and how to create Data Definition Language statements to create or update a database running in SQL Server, OracTable of Contents1. Overview of Databases 2. Normalization 3. Database Implementation 4. Normalization and Physical Design Exercise 5. The erwin Data Modeling Tool 6. Using Microsoft Access 7. Using SQL Server 8. Using Perl to Extract and Load Data 9. Building User Interfaces 10. Creating the University Database Application 11. PHP Implementation and Used

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Book SynopsisUsing data from one season of NBA games, Basketball Data Science: With Applications in R is the perfect book for anyone interested in learning and applying data analytics in basketball. Whether assessing the spatial performance of an NBA player''s shots or doing an analysis of the impact of high pressure game situations on the probability of scoring, this book discusses a variety of case studies and hands-on examples using a custom R package. The codes are supplied so readers can reproduce the analyses themselves or create their own. Assuming a basic statistical knowledge, Basketball Data Science with R is suitable for students, technicians, coaches, data analysts and applied researchers.Features: One of the first books to provide statistical and data mining methods for the growing field of analytics in basketball Presents tools for modelling graphs and figures to visualize the data Includes real woTrade Review"This book provides a unique insight into the use of Statistics in Basketball. I am not aware of any similar text and this is a much welcomed book. It covers applications to Basketball of a good number of statistical methods. The book starts by describing the different types of data in Basketball and how to create summary statistics and different plots. Several advanced methods are described later to exploit the available information and discover patterns in the data. Furthermore, FOCUS sections throughout the book provide interesting case studies on important aspects of the game. The associated R package BasketballAnalyzeR, developed by the authors, is extensively used in the book to develop the examples. This book will be of interest to those working in sport data science as well as those with a passion for Basketball." –Virgilio Gomez Rubio From the forward: "I am grateful to [the authors] for sharing this ‘philosophical’ approach in their valuable work. I think that it is the correct route for bringing [coaches and analysts] closer together and achieving the maximum pooling of knowledge."–Ettore Messina, Head Coach, Olimpia Militano, former Assistant Coach, San Antonio Spurs "Overall, I think this is an excellent book and it was super fun to read. It will certainly have an impact on the sports data science community." –Patrick Mair, Harvard University "The analysis is sophisticated but well-grounded. The depth of the authors' training in statistical methodology and experience analyzing data comes through clearly, filling the readers with confidence. In writing this practical but fascinating book, they have brought this expertise to bear on quantifying basketball in a way that could be indispensable for coaches, players and analysts, and tremendously interesting for fans." –Jason Osborne, North Carolina State University "My overall impression of Basketball Data Science with Applications in R is that it's exactly the sort of book I would recommend to an instructor or able student of statistics in sport" –Jack Davis, Simon Fraser University "This book I know by heart and like it very much. It is a nice collection of data science methods for basketball analysis combined with software code examples (in the statistical programming language R)."–Prof. Dr. Andreas Groll, Technische Universität Dortmund "This book provides a unique insight into the use of Statistics in Basketball. I am not aware of any similar text and this is a much welcomed book. It covers applications to Basketball of a good number of statistical methods. The book starts by describing the different types of data in Basketball and how to create summary statistics and different plots. Several advanced methods are described later to exploit the available information and discover patterns in the data. Furthermore, FOCUS sections throughout the book provide interesting case studies on important aspects of the game. The associated R package BasketballAnalyzeR, developed by the authors, is extensively used in the book to develop the examples. This book will be of interest to those working in sport data science as well as those with a passion for Basketball." –Virgilio Gomez Rubio From the foreword: "I am grateful to [the authors] for sharing this ‘philosophical’ approach in their valuable work. I think that it is the correct route for bringing [coaches and analysts] closer together and achieving the maximum pooling of knowledge."–Ettore Messina, Head Coach, Olimpia Militano, former Assistant Coach, San Antonio Spurs "Overall, I think this is an excellent book and it was super fun to read. It will certainly have an impact on the sports data science community." –Patrick Mair, Harvard University "The analysis is sophisticated but well-grounded. The depth of the authors' training in statistical methodology and experience analyzing data comes through clearly, filling the readers with confidence. In writing this practical but fascinating book, they have brought this expertise to bear on quantifying basketball in a way that could be indispensable for coaches, players and analysts, and tremendously interesting for fans." –Jason Osborne, North Carolina State University "My overall impression of Basketball Data Science with Applications in R is that it's exactly the sort of book I would recommend to an instructor or able student of statistics in sport" –Jack Davis, Simon Fraser University “The real strength of this book is that it is meant to be hands-on. As part of the text, the authors provide access to a custom-built package in R, along with an excellent pre-prepared data set (one full season’s worth of NBA box score and play-by-play data). The authors then guide the reader through many examples of building graphs and tables using their R package and data. The graphs are often intricate and visually detailed, but the text shows how to make them quickly, giving detailed instructions. I imagine that a reader looking to get into basketball analysis could find this book very exciting, because it provides a quick and easy entry point into conducting sophisticated analyses and making visually arresting graphs and figures. A reader can easily follow along and replicate everything that is done in the book. Or, what is more likely, the reader can skim through the text until they come to a plot that looks particularly cool, and then by reading the surrounding section they can quickly learn how to do such an analysis for themselves.” –Brian Skinner, MIT "This book I know by heart and like it very much. It is a nice collection of data science methods for basketball analysis combinedwith software code examples (in the statistical programming language R)."–Prof. Dr. Andreas Groll, Technische Universität Dortmund "For those interested in any level of statistical data analysis in basketball, specifically in R, Basketball Data Science: With Applications in R would be a valuable addition to their library. Further, this text would be quite useful for a course in sports data focusing on basketball or for a student’s research project." Russ Goodman, Central College, Iowa, USA, Mathematical Association of America, April 2023. Table of Contents1. Introduction. 2. Finding Groups in Data. 3. Finding Structures in Data with Machine Learning. 4. Modelling Relationships in Basketball. 5. Concluding Remarks and Future Perspectives.

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Book SynopsisIn order to make informed decisions, there are three important elements: intuition, trust, and analytics. Intuition is based on experiential learning and recent research has shown that those who rely on their gut feelings may do better than those who don't. Analytics, however, are important in a data-driven environment to also inform decision making. The third element, trust, is critical for knowledge sharing to take place. These three elementsintuition, analytics, and trustmake a perfect combination for decision making. This book gathers leading researchers who explore the role of these three elements in the process of decision-making.Table of ContentsIntuition. The Underpinnings of Intuition. How Intuition Affects Decision Making. Data, Insights, Models, and Decisions. The Missing Link—Experiential Learning. Cases of Intuition Outperforming Analytics. Trust. The Foundation of Trust. Trust and Organizational Leadership. Trust and Knowledge Sharing. Trust and Organizational Communication. Trust and Marketing. Trust and Social Media. Analytics. The Secret Sauce. Predictive Analytics. Prescriptive Analytics. Developing an Analytics Strategy. Looking Toward the Future with Cognitive Computing and AI.

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Book SynopsisFuzzy logic principles, practices, and real-world applicationsThis hands-on guide offers clear explanations of fuzzy logic along with practical applications and real-world examples. Written by an award-winning engineer, Fuzzy Logic: Applications in Artificial Intelligence, Big Data, and Machine Learning is aimed at improving competence and motivation in students and professionals alike.Inside, you will discover how to apply fuzzy logic in the context of pervasive digitization and big data across emerging technologies which require a very different man-machine relationship than the ones previously used in engineering, science, economics, and social sciences. Applications covered include intelligent energy systems with demand response, smart homes, electrification of transportation, supply chain efficiencies, smart cities, e-commerce, education, healthcare, and decarbonization.Serves as a classroom guide and as an on-the-job resource

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Book SynopsisDelivering concise, cutting-edge coverage, CONCEPTS OF DATABASE MANAGEMENT, 8e uses real-world cases, examples, and illustrations to give students a thorough understanding of such critical issues as database design, data integrity, concurrent updates, data security, and more. Completely updated to Microsoft Access 2013 standards, the text presents SQL in a database-neutral environment and covers all major topics, including E-R diagrams, normalization, and database design. It provides detailed coverage of the relational model (including QBE and SQL), normalization and views, database administration and management, and more. Advanced topics covered include distributed databases, data warehouses, stored procedures, triggers, data macros, and Web Apps. Ideal for an introductory database course in an information systems, business, or CIS program, CONCEPTS OF DATABASE MANAGEMENT, 8e can be used in varying disciplines by instructors who want database coverage without using a trade book or a lTable of Contents1. Introduction to Database Management. 2. The Relational Model 1: Introduction, QBE, and Relational Algebra. 3. The Relational Model 2: SQL. 4. The Relational Model 3: Advanced Topics. 5. Database Design 1: Normalization. 6. Database Design 2: Design Method. 7. DBMS Functions. 8. Database Administration. 9. Database Management Approaches. Appendix A: Comprehensive Design Example: Marvel College. Appendix B: SQL Reference. Appendix C: How Do I" Reference. Appendix D: Answers to Odd-Numbered Review Questions. Appendix E: Access Web Apps. Appendix F: Systems Analysis Approach to Information-Level Requirements."

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Book SynopsisTable of Contents1 Introduction 11.1 Fundamental Concepts . . . . . . . . . . . . . . . . . . . . . 21.2 Access Control Models . . . . . . . . . . . . . . . . . . . . . 191.3 Cryptographic Concepts . . . . . . . . . . . . . . . . . . . . . 251.4 Implementation and Usability Issues . . . . . . . . . . . . . . 391.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 462 Physical Security 552.1 Physical Protections and Attacks . . . . . . . . . . . . . . . . 562.2 Locks and Safes . . . . . . . . . . . . . . . . . . . . . . . . . 572.3 Authentication Technologies . . . . . . . . . . . . . . . . . . . 712.4 Direct Attacks Against Computers . . . . . . . . . . . . . . . 882.5 Special-Purpose Machines . . . . . . . . . . . . . . . . . . . 992.6 Physical Intrusion Detection . . . . . . . . . . . . . . . . . . . 132.7 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 3 Operating Systems Security 1133.1 Operating Systems Concepts . . . . . . . . . . . . . . . . . . 114 3.2 Process Security . . . . . . . . . . . . . . . . . . . . . . . . . 1303.3 Memory and Filesystem Security . . . . . . . . . . . . . . . . 136 3.4 Application Program Security . . . . . . . . . . . . . . . . . . 1493.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166 4 Malware 173 4.1 Insider Attacks . . . . . . . . . . . . . . . . . . . . . . . . . . 1744.2 Computer Viruses . . . . . . . . . . . . . . . . . . . . . . . . 1814.3 Malware Attacks . . . . . . . . . . . . . . . . . . . . . . . . . 1884.4 Privacy-Invasive Software . . . . . . . . . . . . . . . . . . . . 202 4.5 Countermeasures . . . . . . . . . . . . . . . . . . . . . . . . 2084.6 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215 5 Network Security I 2215.1 Network Security Concepts . . . . . . . . . . . . . . . . . . . 2225.2 The Link Layer . . . . . . . . . . . . . . . . . . . . . . . . . . 2295.3 The Network Layer . . . . . . . . . . . . . . . . . . . . . . . . 2365.4 The Transport Layer . . . . . . . . . . . . . . . . . . . . . . . 2465.5 Denial-of-Service Attacks . . . . . . . . . . . . . . . . . . . . 256 5.6 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264 6 Network Security II 2696.1 The Application Layer and DNS . . . . . . . . . . . . . . . . . 2706.2 Firewalls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2876.3 Tunneling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 292 6.4 Intrusion Detection . . . . . . . . . . . . . . . . . . . . . . . . 2996.5 Wireless Networking . . . . . . . . . . . . . . . . . . . . . . . 313 6.6 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 322 7 Web Security 3277.1 The World Wide Web . . . . . . . . . . . . . . . . . . . . . . 3287.2 Attacks on Clients . . . . . . . . . . . . . . . . . . . . . . . . 347 7.3 Attacks on Servers . . . . . . . . . . . . . . . . . . . . . . . . 3687.4 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 382 8 Cryptography 3878.1 Symmetric Cryptography . . . . . . . . . . . . . . . . . . . . 3888.2 Public-Key Cryptography . . . . . . . . . . . . . . . . . . . . . 4068.3 Cryptographic Hash Functions . . . . . . . . . . . . . . . . . 4178.4 Digital Signatures . . . . . . . . . . . . . . . . . . . . . . . . . 4218.5 Details on AES and RSA . . . . . . . . . . . . . . . . . . . . 4258.6 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 439 9 Distributed-Applications Security 4879.1 Database

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Book SynopsisFrank M. Carrano is Professor Emeritus of Computer Science at the University of Rhode Island. He received his Ph.D. degree in Computer Science from Syracuse University in 1969. His interests include data structures, computer science education, social issues in computing, and numerical computation. Professor Carrano is particularly interested in the design and delivery of undergraduate courses in computer science. He has authored several well-known computer science textbooks for undergraduates. Timothy M. Henry has a Bachelor of Science Degree in Mathematics from the U.S. Coast Guard Academy, a Master of Science Degree in Computer Science from Old Dominion University, and was awarded a PhD in Applied Math Sciences from the University of Rhode Island. He began his IT career as an officer in the U.S. Coast Guard, and among his early tours, he was the Information Resources Manager (what is today a CIO) at the Coast Guard's training centre in Yorktown, VA.Table of Contents Introduction Chapter 1: Bags Chapter 2: Bag Implementations That Use Arrays Chapter 3: A Bag Implementation That Links Data Chapter 4: The Effciency of Algorithms Chapter 5: Stacks Chapter 6: Stack Implementations Chapter 7: Recursion Chapter 8: An Introduction to Sorting Chapter 9: Faster Sorting Methods Chapter 10: Queues, Deques, and Priority Queues Chapter 11: Queue, Deque, and Priority Queue Implementations Chapter 12: Lists Chapter 13: A List Implementation That Uses an Array Chapter 14: A List Implementation That Links Data Chapter 15: Iterators for the ADT List Chapter 16: Sorted Lists Chapter 17: Inheritance and Lists Chapter 18: Searching Chapter 19: Dictionaries Chapter 20: Dictionary Implementations Chapter 21: Introducing Hashing Chapter 22: Hashing as a Dictionary Implementation Chapter 23: Trees Chapter 25: A Binary Search Tree Implementation Chapter 26: A Heap Implementation Chapter 27: Balanced Search Trees Chapter 28: Graphs Chapter 29: Graph Implementations

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

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Book SynopsisGain a thorough, applied understanding of critical database issues with Starks/Pratt/Last's CONCEPTS OF DATABASE MANAGEMENT, 9E. Real cases, examples and screenshots in this concise presentation help clarify database design, data integrity, normalization, concurrent updates, data security, and big data. Completely updated to SQL Server 2016, Microsoft Access 2016, and Office 365 standards, this edition explores SQL in a database-neutral environment while addressing E-R diagrams, normalization, and database design. Detailed coverage presents the relational model (including QBE and SQL), normalization and views, database administration and management. You also examine advanced topics such as distributed databases, data warehouses, stored procedures, triggers, data macros and Web Apps. This introduction to database is ideal for mastering today's database techniques.Table of Contents1. Introduction to Database Management. 2. The Relational Model 1: Introduction, QBE, and Relational Algebra. 3. The Relational Model 2: SQL. 4. The Relational Model 3: Advanced Topics. 5. Database Design 1: Normalization. 6. Database Design 2: Design Method. 7. DBMS Functions. 8. Database Administration. 9. Database Management Approaches. Appendix A: Comprehensive Design Example: Marvel College. Appendix B: SQL Reference. Appendix C: MySQL. Appendix D: How Do I" Reference. Appendix E: Using Access to Create a Web App. Appendix F: A Systems Analysis Approach to Information-Level Requirements."

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Book SynopsisIntroducing SQLite.- Getting Started.- SQL for SQLite.- Advanced SQL for SQLite.- SQLite Design and Concepts.- The Core C API.- The Extension C API.- Language Extensions.- iOS Development with SQLite.- Android Development with SQLite.- SQLite Internals and New Features.Table of Contents Introducing SQLite Getting Started SQL for SQLite Advanced SQL for SQLite SQLite Design and Concepts The Core C API The Extension C API Language Extensions iOS Development with SQLite Android Development with SQLite SQLite Internals and New Features

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

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Book SynopsisBeginning Oracle SQL is your introduction to the interactive query tools and specific dialect of SQL used with Oracle Database.Table of Contents1. Relational Database Systems and Oracle2. Introduction to SQL and SQL*Plus, and SQL Developer3. Data Definition, Part I4. Retrieval: The Basics5. Retrieval: Functions6. Data Manipulation7. Data Definition, Part II8. Retrieval: Joins and Grouping9. Retrieval: Advanced Features10. Views11. Automating12. Object-Relational Features13. Appendix A – Case Tables14. Appendix B – Exercise Solutions

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Book SynopsisAn emerging topic in software engineering and data mining, specification mining tackles software maintenance and reliability issues that cost economies billions of dollars each year. The first unified reference on the subject, Mining Software Specifications: Methodologies and Applications describes recent approaches for mining specifications of software systems. Experts in the field illustrate how to apply state-of-the-art data mining and machine learning techniques to address software engineering concerns.In the first set of chapters, the book introduces a number of studies on mining finite state machines that employ techniques, such as grammar inference, partial order mining, source code model checking, abstract interpretation, and more. The remaining chapters present research on mining temporal rules/patterns, covering techniques that include path-aware static program analyses, lightweight rule/pattern mining, statistical analysis, and other interesting apTable of ContentsSpecification Mining: A Concise Introduction. Mining Finite-State Automata with Annotations. Adapting Grammar Inference Techniques to Mine State Machines. Mining API Usage Protocols from Large Method Traces. Static API Specification Mining: Exploiting Source Code Model Checking. Static Specification Mining Using Automata-Based Abstractions. DynaMine: Finding Usage Patterns and Their Violations by Mining Software Repositories. Automatic Inference and Effective Application of Temporal Specifications. Path-Aware Static Program Analyses for Specification Mining. Mining API Usage Specifications via Searching Source Code from the Web. Merlin: Specification Inference for Explicit Information Flow Problems. Lightweight Mining of Object Usage.

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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 SynopsisThe enthusiasm behind Node doesn't just reflect the promise of server-side JavaScript. Developers also have the potential to create elegant applications with this open source framework that are much easier to maintain.

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Book SynopsisHow can you take advantage of feedback control for enterprise programming? With this book, author Philipp K. Janert demonstrates how the same principles that govern cruise control in your car also apply to data center management and other enterprise systems.

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Book SynopsisWith this practical book, you will learn proven methods for anonymizing health data to help your organization share meaningful datasets, without exposing patient identity. Leading experts Khaled El Emam and Luk Arbuckle walk you through a risk-based methodology, using case studies from their efforts to de-identify hundreds of datasets.

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Book SynopsisBrings Semantic Web practice to enterprise. Fabien Gandon joins Dean Allemang and Jim Hendler, to open up the story to a modern view of global linked data. Examples have been brought up to date and applied in a modern setting, where enterprise and global data come together as a living, linked network of data.Table of Contents Preface What is the Semantic Web? Semantic modeling RDF—the basis of the Semantic Web Semantic Web application architecture Linked data Querying the Semantic Web—SPARQL Extending RDF: RDFS and SCHACL RDF Schema RDFS-Plus Using RDFS-Plus in the wild SKOS—managing vocabularies with RDFS-Plus Basic OWL Counting and sets in OWL Ontologies on the Web—putting it all together Good and bad modeling practices Expert modeling in OWL Conclusions and future work Bibliography

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Book SynopsisBrings Semantic Web practice to enterprise. Fabien Gandon joins Dean Allemang and Jim Hendler, to open up the story to a modern view of global linked data. Examples have been brought up to date and applied in a modern setting, where enterprise and global data come together as a living, linked network of data.Table of Contents Preface What is the Semantic Web? Semantic modeling RDF—the basis of the Semantic Web Semantic Web application architecture Linked data Querying the Semantic Web—SPARQL Extending RDF: RDFS and SCHACL RDF Schema RDFS-Plus Using RDFS-Plus in the wild SKOS—managing vocabularies with RDFS-Plus Basic OWL Counting and sets in OWL Ontologies on the Web—putting it all together Good and bad modeling practices Expert modeling in OWL Conclusions and future work Bibliography

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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 SynopsisAmbient Diagnostics addresses innovative methods for discovering patterns from affordable devices, such as mobile phones, watches, cameras, and game interfaces, to interpret multimedia data for personal health monitoring and diagnosis. This is the first comprehensive textbook on multidisciplinary innovations in affordable healthcarefrom sensory fusion, pattern detection, to classification.Connecting the DotsThe material in this book combines sensing, pattern recognition, and visual design, and is divided into four parts, which cover fundamentals, multimedia intelligence, pervasive sensors, and crowdsourcing. The author describes basic pattern discovery models, sound, color, motion and video analytics, and pattern discovery from games and social networks. Each chapter contains the material's main concepts, as well as case studies, and extensive study questions. Trade Review"... not only the first book of its kind, it's the only reference I know of that shows how designers and engineers might create a new world of ambient healthcare with non-invasive, low-cost and accessible sensors. Yang Cai's book is a very readable, extremely comprehensive collection of technologies and applications for readily accessible sensors and mobile technologies for immediate application in everyday diagnosis across the spectrum of health situations. Yang Cai's book is especially valuable as a deeply-researched and reliable source for engineers, product and medical device designers to quickly learn and adapt the emerging tools and sensors to facilitate what is so brilliantly framed as "ambient diagnostics." -Peter Jones, PhD, OCAD University, Toronto "The main contribution of this book is a comprehensive transversal approach that combines this ample background into a single matter ... a combination of diverse methodologies and strategies that are directly applicable to sense, perceive and recognize a great diversity of ambient parameters. Its easy-to-read style and its frequent references to real life examples make the book very attractive to the reader." -Julio Abascal, University of the Basque Country/Euskal Herriko UnibertsitateaTable of Contents Part I. Fundamentals. Introduction. Transformation. Pattern Recognition. Part II. Multimedia Intelligence. Sound Recognition. Color Sensors. Kinect Sensors. Video Analytics. Fatigue Sensing. Part III. Pervasive Sensors. Mobile Sensors. Body Media. Pocket Microscopes. Personal Spectrometers. Part IV. Crowd Sourcing. Remote Sensing. Games for Diagnosis. Social Media. Problems. Sample Source Code. Further Readings. Index.

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Book SynopsisA User''s Guide to Business Analytics provides a comprehensive discussion of statistical methods useful to the business analyst. Methods are developed from a fairly basic level to accommodate readers who have limited training in the theory of statistics. A substantial number of case studies and numerical illustrations using the R-software package are provided for the benefit of motivated beginners who want to get a head start in analytics as well as for experts on the job who will benefit by using this text as a reference book.The book is comprised of 12 chapters. The first chapter focuses on business analytics, along with its emergence and application, and sets up a context for the whole book. The next three chapters introduce R and provide a comprehensive discussion on descriptive analytics, including numerical data summarization and visual analytics. Chapters five through seven discuss set theory, definitions and counting rules, probability, random Table of ContentsWhat Is Analytics? Introducing R—An Analytics Software. Reporting Data. Statistical Graphics and Visual Analytics. Probability. Random Variables and Probability Distributions. Continuous Random Variables. Statistical Inference. Regression for Predictive Model Building. Decision Trees. Data Mining and Multivariate Methods. Modeling Time Series Data for Forecasting.

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Book SynopsisThe Shakespearean Inside is a study of all soliloquies and solo asides (dubbed insides for short) in Shakespeare's complete plays.

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Book SynopsisThe Shakespearean Inside' is a study of all soliloquies and solo asides (dubbed insides for short) in Shakespeare's complete plays.

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Book SynopsisAlthough the terms data mining and knowledge discovery and data mining (KDDM) are sometimes used interchangeably, data mining is actually just one step in the KDDM process. Data mining is the process of extracting useful information from data, while KDDM is the coordinated process of understanding the business and mining the data in order to identify previously unknown patterns.Knowledge Discovery Process and Methods to Enhance Organizational Performance explains the knowledge discovery and data mining (KDDM) process in a manner that makes it easy for readers to implement. Sharing the insights of international KDDM experts, it details powerful strategies, models, and techniques for managing the full cycle of knowledge discovery projects. The book supplies a process-centric view of how to implement successful data mining projects through the use of the KDDM process. It discusses the implications of data mining including security, privacy, ethical and legal consideratioTable of ContentsIntroduction to Reinforcement Learning. Model-Free Policy Iteration. Policy Iteration with Value Function Approximation. Basis Design for Value Function Approximation. Sample Reuse in Policy Iteration. Active Learning in Policy Iteration. Robust Policy Iteration. Model-Free Policy Search. Direct Policy Search by Gradient Ascent. Direct Policy Search by Expectation-Maximization. Policy-Prior Search. Model-Based Reinforcement Learning. Transition Model Estimation. Dimensionality Reduction for Transition Model Estimation.

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Book SynopsisData Mining with R: Learning with Case Studies, Second Edition uses practical examples to illustrate the power of R and data mining. Providing an extensive update to the best-selling first edition, this new edition is divided into two parts. The first part will feature introductory material, including a new chapter that provides an introduction to data mining, to complement the already existing introduction to R. The second part includes case studies, and the new edition strongly revises the R code of the case studies making it more up-to-date with recent packages that have emerged in R.The book does not assume any prior knowledge about R. Readers who are new to R and data mining should be able to follow the case studies, and they are designed to be self-contained so the reader can start anywhere in the document. The book is accompanied by a set of freely available R source files that can be obtained at the book's web site. These files inTable of ContentsIntroduction. I R AND DATA MINING. Introduction to R. Introduction to Data Mining. II CASE STUDIES. Predicting Algae Blooms. Predicting Stock Market Returns. Detecting Fraudulent Transactions. Classifying Microarray Samples

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

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Book 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 SynopsisThis book describes the creation of an actual generic open source big data stack, which is an integrated stack of big data components--each of which serves a specific function like storage, resource management, or queueing. Each component has a big data heritage and community to support it. It can support big data in that it is able to scale, and it is a distributed and robust system.In the Complete Guide to Open Source Big Data Stack, Mike Frampton begins by creating a private cloud and then by installing and examining Apache Brooklyn. After that he will use each chapter to introduce one piece of the big data stacksharing how to source the software and then how to install it. He will then show how it works by simple example. Step by step and chapter by chapter, Frampton will create a real big data stack. The goal of this book is to show how a big data stack might be created and what components might be used. It attempts to do this with currently available ApaTable of ContentsChapter 1: The Big Data Stack Overview.- Chapter 2: Cloud Storage.- Chapter 3: Apache Brooklyn.- Chapter 4: Apache Mesos.- Chapter 5: Stack Storage Options.- Chapter 6: Processing.- Chapter 7: Streaming.- Chapter 8: Frameworks.- Chapter 9: Visualization.- Chapter 10: The Big Data Stack.-

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Book SynopsisChapter 1: Overview.- Chapter 2: Blogalytics.- Chapter 3: Getting Traffic for Analytics.- Chapter 4: Reviewing Performance of Campaigns.- Chapter 5: Fun with eCommerce Analytics Part I: Shopify.- Chapter 6: Fun with eCommerce Analytics Part II: AdWords.- Chapter 7: Fun with eCommerce Analytics Part III: Gumroad.- Chapter 8: Exploring Google Analytics Certification.Table of ContentsChapter 1: Overview Chapter 2: Blogalytics Chapter 3: Getting Traffic for Analytics Chapter 4: Reviewing Performance of Campaigns Chapter 5: Fun with eCommerce Analytics Part I: Shopify Chapter 6: Fun with eCommerce Analytics Part II: AdWords Chapter 7: Fun with eCommerce Analytics Part III: Gumroad Chapter 8: Exploring Google Analytics Certification

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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 SynopsisModel, map, and access data effectively with Entity Framework Core 2, the latest evolution of Microsoft''s object-relational mapping framework. You will access data utilizing .NET objects via the most common data access layer used in ASP.NET Core MVC 2 projects.  Best-selling author Adam Freeman explains how to get the most from Entity Framework Core 2 in MVC projects. He begins by describing the different ways that Entity Framework Core 2 can model data and the different types of databases that can be used. He then shows you how to use Entity Framework Core 2 in your own MVC projects, starting from the nuts and bolts and building up to the most advanced and sophisticated features, going in-depth to give you the knowledge you need. Chapters include common problems and how to avoid them. What You''ll Learn Gain a solid architectural understanding of Entity Framework Core 2 CrTable of ContentsPart 1------1 - Entity Framework Core in Context2 - Your First Entity Framework Core Application3 - Working with Databases4 - SportsStore - A Real (Data) Application5 - SportsStore - Storing Data6 - SportsStore - Modifying Data7 - SportsStore - Expanding the Data Model8 - SportsStore - Scaling Up9 - SportsStore - Customer Features10 - SportsStore - Creating An APIPart 2-----11 - Working with Entity Framework Core12 - Performing Data Operations13 - Understanding Migrations14 - Creating Data Relationships15 - Working with Relationships, Part 116 - Working with Relationships, Part 217 - Scaffolding an Existing Database18 - Manually Modelling a DatabasePart 3-----19 - Keys20 - Querying Data21 - Storing Data22 - Deleting Data23 - Using Database Server Features24 - Using Transactions

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Book SynopsisMigrate your data to Salesforce and build low-maintenance and high-performing data integrations to get the most out of Salesforce and make it a go-to place for all your organization''s customer information.When companies choose to roll out Salesforce, users expect it to be the place to find any and all Information related to a customer-the coveted Client 360 view. On the day you go live, users expect to see all their accounts, contacts, and historical data in the system. They also expect that data entered in other systems will be exposed in Salesforce automatically and in a timely manner. This book shows you how to migrate all your legacy data to Salesforce and then design integrations to your organization''s mission-critical systems. As the Salesforce platform grows more powerful, it also grows in complexity. Whether you are migrating data to Salesforce, or integrating with Salesforce, it is important to understand how these complexities need to be reflected in your desiTable of Contents

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Book SynopsisStart developing with Oracle SQL. This book is a one-stop introduction to everything you need to know about getting started developing an Oracle Database. You''ll learn about foundational concepts, setting up a simple schema, adding data, reading data from the database, and making changes. No experience with databases is required to get started. Examples in the book are built around Oracle Live SQL, a freely available, online sandbox for practicing and experimenting with SQL statements, and Oracle Express Edition, a free version of Oracle Database that is available for download.A marquee feature of Beginning Oracle SQL for Oracle Database 18c is the small chapter size. Content is divided into easily digestible chunks that can be read and practiced in very short intervals of time, making this the ideal book for a busy professional to learn from. Even just a 15-20 minute block of free time can be put to good use.AuthoTable of ContentsIntroductionPart I. Setting Up1. What Is A Database?2. Setting UpPart II. Viewing Data3. Retrieving Data4. Selecting Specific Columns5. Restricting the Results6. Comparing Data7. Applying Multiple Filters8. Working with NULLs9. Removing Duplicate Results10. Applying Filters on Lists and Ranges of Values11. Ordering Your Data12. Applying Table and Column AliasesPart III. Adding, Updating, Deleting Data13. Understanding the Data Types14. Creating A Table15. Adding Data to A Table16. Updating and Removing Data17. Updating or Deleting A TablePart IV. Joining Tables18. Inner Join19. Outer Join20. Other Join Types21. Joining Many Tables28. Understanding the Alternative Join SyntaxPart V. Functions22. Using Functions in SQL23. Writing Conditional Logic24. Understanding Aggregate Functions25. Grouping Your Results 26. What Are Indexes?Part VI. Command Line27. Using the Command LinePart VII. Appendixes28. Appendix. How to Find and Navigate the Oracle SQL Reference

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

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