Databases / Data management Books

Book SynopsisAPCOM is a peer-reviewed forum for industrial and research communities working in the mineral industry to share expertise on the application of computer and operations research technology. Recognized since the 1960s as the world's premier conference in the field, APCOM features an impressive range of topics from geostatistics to data warehousing. APCOM 2005 builds on this reputation, showcasing the latest industrial applications and emerging technologies, focusing particularly on mobilizing the inherent value in largely under-used data and information systems, and how these data systems cab be analyzed for real-time or process-based improvements.

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Book SynopsisJulie C. Meloni is a technical consultant who has been developing web-based applications since the Web first saw the light of day. She has authored numerous books and articles on web-based programming and scripting languages and database topics, and you can find translations of her work in 18 different languages.  Table of ContentsPart I: Web Application Basics CHAPTER 1: Understanding How the Web Works A Brief History of HTML and the World Wide Web Creating Web Content Understanding Web Content Delivery Selecting a Web Hosting Provider Testing with Multiple Web Browsers Creating a Sample File Using FTP to Transfer Files Understanding Where to Place Files on the Web Server CHAPTER 2: Structuring HTML and Using Cascading Style Sheets Getting Started with a Simple Web Page HTML Tags Every Web Page Must Have Using Hyperlinks in Web Pages Organizing a Page with Paragraphs and Line Breaks Organizing Your Content with Headings Understanding Semantic Elements How CSS Works A Basic Style Sheet A CSS Style Primer Using Style Classes Using Style IDs Internal Style Sheets and Inline Styles CHAPTER 3: Understanding the CSS Box Model and Positioning The CSS Box Model The Whole Scoop on Positioning Controlling the Way Things Stack Up Managing the Flow of Text Understanding Fixed Layouts Understanding Fluid Layouts Creating a Fixed/Fluid Hybrid Layout Considering a Responsive Web Design CHAPTER 4: Introducing JavaScript Learning Web Scripting Basics How JavaScript Fits into a Web Page Exploring JavaScript’s Capabilities Basic JavaScript Language Concepts JavaScript Syntax Rules Using Comments Best Practices for JavaScript Understanding JSON Using the JavaScript Console to Debug JavaScript CHAPTER 5: Introducing PHP How PHP Works with a Web Server The Basics of PHP Scripts Code Blocks and Browser Output Part II: Getting Started with Dynamic Websites CHAPTER 6: Understanding Dynamic Websites and HTML5 Applications Refresher on the Different Types of Scripting Displaying Random Content on the Client Side Understanding the Document Object Model Using window Objects Working with the document Object Accessing Browser History Working with the location Object More About the DOM Structure Working with DOM Nodes Creating Positionable Elements (Layers) Hiding and Showing Objects Modifying Text Within a Page Adding Text to a Page Changing Images Based on User Interaction Thinking Ahead to Developing HTML5 Applications CHAPTER 7: JavaScript Fundamentals: Variables, Strings, and Arrays Using Variables Understanding Expressions and Operators Data Types in JavaScript Converting Between Data Types Using String Objects Working with Substrings Using Numeric Arrays Using String Arrays Sorting a Numeric Array CHAPTER 8: JavaScript Fundamentals: Functions, Objects, and Flow Control Using Functions Introducing Objects Using Objects to Simplify Scripting Extending Built-in Objects Using the Math Object Working with Math Methods Working with Dates The if Statement Using Shorthand Conditional Expressions Testing Multiple Conditions with if and else Using Multiple Conditions with switch Using for Loops Using while Loops Using do…while Loops Working with Loops Looping Through Object Properties CHAPTER 9: Understanding JavaScript Event Handling Understanding Event Handlers Using Mouse Events Using Keyboard Events Using the load and unload Events CHAPTER 10: The Basics of Using jQuery Using Third-Party JavaScript Libraries jQuery Arrives on the Scene Preparing to Use jQuery Becoming Familiar with the $().ready Handler Selecting DOM and CSS Content Manipulating HTML Content Putting the Pieces Together to Create a jQuery Animation Handling Events with jQuery Part III: Taking Your Web Applications to the Next Level CHAPTER 11: AJAX: Remote Scripting Introducing AJAX Using XMLHttpRequest Creating a Simple AJAX Library Creating an AJAX Quiz Using the Library Debugging AJAX-Based Applications Using jQuery’s Built-in Functions for AJAX CHAPTER 12: PHP Fundamentals: Variables, Strings, and Arrays Variables Data Types Using Expressions and Operators Constants Understanding Arrays Creating Arrays Some Array-Related Constructs and Functions CHAPTER 13: PHP Fundamentals: Functions, Objects, and Flow Control Calling Functions Defining a Function Returning Values from User-Defined Functions Understanding Variable Scope Saving State Between Function Calls with the static Statement More About Arguments Testing for the Existence of a Function Creating an Object Object Inheritance Switching Flow Implementing Loops CHAPTER 14: Working with Cookies and User Sessions Introducing Cookies Setting a Cookie Deleting a Cookie Overview of Server-Side Sessions Working with Session Variables Destroying Sessions and Unsetting Session Variables Using Sessions in an Environment with Registered Users CHAPTER 15: Working with Web-Based Forms How HTML Forms Work Creating a Form Accepting Text Input Naming Each Piece of Form Data Labeling Each Piece of Form Data Grouping Form Elements Exploring Form Input Controls Using HTML5 Form Validation Submitting Form Data Accessing Form Elements with JavaScript Accessing Form Elements with PHP Using Hidden Fields to Save State in Dynamic Forms Sending Mail on Form Submission Part IV: Integrating a Database into Your Applications CHAPTER 16: Understanding the Database Design Process The Importance of Good Database Design Types of Table Relationships Understanding Normalization Following the Design Process CHAPTER 17: Learning Basic SQL Commands Learning the MySQL Data Types Learning the Table-Creation Syntax Using the INSERT Statement Using the SELECT Statement Using WHERE in Your Queries Selecting from Multiple Tables Using the UPDATE Statement to Modify Records Using the REPLACE Statement Using the DELETE Statement Frequently Used String Functions in MySQL Using Date and Time Functions in MySQL CHAPTER 18: Interacting with MySQL Using PHP MySQL or MySQLi? Connecting to MySQL with PHP Working with MySQL Data Part V: Getting Started with Application Development CHAPTER 19: Creating a Simple Discussion Forum Designing the Database Tables Creating an Include File for Common Functions Creating the Input Forms and Scripts Displaying the Topic List Displaying the Posts in a Topic Adding Posts to a Topic Modifying the Forum Display with JavaScript CHAPTER 20: Creating an Online Storefront Planning and Creating the Database Tables Displaying Categories of Items Displaying Items Using JavaScript with an Online Storefront CHAPTER 21: Creating a Simple Calendar Building a Simple Display Calendar Creating the Calendar in JavaScript CHAPTER 22: Managing Web Applications Understanding Some Best Practices in Web Application Development Writing Maintainable Code Implementing Version Control in Your Work Understanding the Value and Use of Code Frameworks Appendixes APPENDIX A: Installation QuickStart Guide with XAMPP APPENDIX B: Installing and Configuring MySQL APPENDIX C: Installing and Configuring Apache APPENDIX D: Installing and Configuring PHP

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Book SynopsisAs Web-based systems and e-commerce carry businesses into the 21st century, databases are becoming workhorses that shoulder each and every online transaction. For organizations to have effective 24/7 Web operations, they need powerhouse databases that deliver at peak performance-all the time. High Performance Web Databases: Design, Development, and Deployment arms you with every essential technique from design and modeling to advanced topics such as data conversion, performance tuning, Web access and interfacing legacy systems, and securityTable of ContentsDatabase Planning and Getting Started. Information Gathering and Analysis. Managing Business Rules. Performance Modeling Methods. Performance Design and Development. Database Integrity and Quality. Distributed Databases, Portability, and Interoperability. Database Integration with the Internet and the Web. Data Migration, Conversion, and Legacy Applications. Performance Tuning. Data Administration and Operations. Data Base Security.

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Book SynopsisThe explosion of Web-based data has created a demand among executives and technologists for methods to identify, gather, analyze, and utilize data that may be of value to corporations and organizations. The emergence of data mining, and the larger field of Web mining, has businesses lost within a confusing maze of mechanisms and strategies for obtaining and managing crucial intelligence.Web Data Mining and Applications in Business Intelligence and Counter-Terrorism responds by presenting a clear and comprehensive overview of Web mining, with emphasis on CRM and, for the first time, security and counter-terrorism applications. The tools and methods of Web mining are revealed in an easy-to-understand style, emphasizing the importance of practical, hands-on experience in the creation of successful e-business solutions.The author, a program director for Data and Applications Security at the National Science Foundations, details how both opportunities and dangers on the WTable of ContentsIntroduction. SUPPORTING TECHNOLOGIES FOR WEB DATA MINING. The World Wide Web and E-Commerce. Data Mining. Core Data Mining Technologies. Web Database Management. Information Retrieval Systems. Information Management Technologies. The Semantic Web. WEB DATA MINING TECHNIQUES, TOOLS, AND TRENDS. Data Mining and the Web. Processes and Techniques for Web Data Mining. Mining the Databases on the Web. Information Retrieval and Web Data Mining. Information Management and Web Data Mining. Semantic Web Mining. Mining Usage Patterns and Structure on the Web. Prototypes, Products, and Standards for Web Data Mining. Some Applications for Web Mining. WEB DATA MINING APPLICATIONS FOR COUNTER-TERRORISM. Some Information on Terrorism, Security Threats, and Protection Measures. Web Data Mining for Counter-Terrorism. Mining the Web Databases for Counter-Terrorism. Information Retrieval and Web Mining for Counter-Terrorism. Information Management and Web Mining for Counter-Terrorism. Semantic Web Mining for Counter-Terrorism. Web Usage and Structure Mining for Counter-Terrorism. National Security, Privacy, Civil Liberties, and Web Mining. Revisiting Security Threats with Respect to Web Mining. E-Commerce, Business Intelligence, and Counter-Terrorism. Summary and Directions. Appendices.

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Book SynopsisThe information age has led to an explosion in the amount of information available to the individual and the means by which it is accessed, stored, viewed, and transferred. In particular, the growth of the internet has led to the creation of huge repositories of multimedia documents in a diverse range of scientific and professional fields, as well as the tools to extract useful knowledge from them.Mining Multimedia Documents is a must-read for researchers, practitioners, and students working at the intersection of data mining and multimedia applications. It investigates various techniques related to mining multimedia documents based on text, image, and video features. It provides an insight into the open research problems benefitting advanced undergraduates, graduate students, researchers, scientists and practitioners in the fields of medicine, biology, production, education, government, national security and economics.Table of ContentsMining Multimedia Documents: An Overview. Fuzzy Decision Trees for Text Document Clustering. Towards Modeling Semi-Automatic Data Warehouses: Guided by Social Interactions. Multi-Agent System for Text Mining. The transformation of User Requirements in UML Diagrams: An Overview. An Overview of Information Extraction using Textual Case-Based Reasoning. Opinions Classification. Documents Classification Based on Text and Image Features. Content-Based Image Retrieval (CBIR). Mining Knowledge in Medical Image Databases. Segmentation for Medical Image Mining. Biological Data Mining: Techniques and Applications. Video Text Extraction and Mining. Recent Advancement in Multimedia Content using Deep Learning.

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Book SynopsisThe Essentials of Data Science: Knowledge Discovery Using R presents the concepts of data science through a hands-on approach using free and open source software. It systematically drives an accessible journey through data analysis and machine learning to discover and share knowledge from data. Building on over thirty years' experience in teaching and practising data science, the author encourages a programming-by-example approach to ensure students and practitioners attune to the practise of data science while building their data skills. Proven frameworks are provided as reusable templates. Real world case studies then provide insight for the data scientist to swiftly adapt the templates to new tasks and datasets. The book begins by introducing data science. It then reviews R's capabilities for analysing data by writing computer programs. These programs are developed and explained step by step. From analysing and visualising data, the framework moves on to tried and tested machine learning techniques for predictive modelling and knowledge discovery. Literate programming and a consistent style are a focus throughout the book.

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Book SynopsisDisk-Based Algorithms for Big Data is a product of recent advances in the areas of big data, data analytics, and the underlying file systems and data management algorithms used to support the storage and analysis of massive data collections. The book discusses hard disks and their impact on data management, since Hard Disk Drives continue to be common in large data clusters. It also explores ways to store and retrieve data though primary and secondary indices. This includes a review of different in-memory sorting and searching algorithms that build a foundation for more sophisticated on-disk approaches like mergesort, B-trees, and extendible hashing. Following this introduction, the book transitions to more recent topics, including advanced storage technologies like solid-state drives and holographic storage; peer-to-peer (P2P) communication; large file systems and query languages like Hadoop/HDFS, Hive, Cassandra, and Presto; and NoSQL databases like Neo4j for graph structurTable of ContentsForeword. Physical Disk Storage. File Management. Sorting. Searching. Disk-Based Sorting. Disk-Based Searching. Storage Technology. Large File Systems. NoSQL Storage. Appendix

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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 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 SynopsisExtremal Optimization: Fundamentals, Algorithms, and Applications introduces state-of-the-art extremal optimization (EO) and modified EO (MEO) solutions from fundamentals, methodologies, and algorithms to applications based on numerous classic publications and the authors' recent original research results. It promotes the movement of EO from academic study to practical applications. The book covers four aspects, beginning with a general review of real-world optimization problems and popular solutions with a focus on computational complexity, such as NP-hard and the phase transitions occurring on the search landscape.Next, it introduces computational extremal dynamics and its applications in EO from principles, mechanisms, and algorithms to the experiments on some benchmark problems such as TSP, spin glass, Max-SAT (maximum satisfiability), and graph partition. It then presents studies on the fundamental features of search dynamics and mechanisms in EO Table of ContentsFUNDAMENTALS, METHODOLOGY, AND ALGORITHMS. General Introduction. Introduction to Extremal Optimization. Extremal Dynamics-Inspired Self-Organizing Optimization. MODIFIED EO AND INTEGRATION OF EO WITH OTHER SOLUTIONS TO COMPUTATIONAL INTELLIGENCE. Modified Extremal Optimization. Memetic Algorithms with Extremal Optimization. Multiobjective Optimization with Extremal Dynamics. APPLICATIONS. EO for Systems Modeling and Control. EO for Production Planning and Scheduling. References.

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Book SynopsisMaintaining the advanced technical focus found in Developing Essbase Applications, this second volume is another collaborative effort by some of the best and most experienced Essbase practitioners from around the world.Developing Essbase Applications: Hybrid Techniques and Practices reviews technology areas that are much-discussed but still very new, including Exalytics and Hybrid Essbase. Covering recent improvements to the Essbase engine, the book illustrates the impact of new reporting and analysis tools and also introduces advanced Essbase best practices across a variety of features, functions, and theories.Some of this book's chapters are in the same vein as the previous volume: hardware, engines, and languages. Others cover new ground with Oracle Business Intelligence Enterprise Edition, design philosophy, benchmarking concepts, and multiple client tools. As before, these subjects are covered from both the technical and best practice perspectives.Trade Review"I enjoyed the first book because it was a collection of best practices, tips, tricks, and mini-guides. ... Reading this book, I was happy to find a wider representation of topics: brand new, forward-looking features like Hybrid Aggregation Mode; popular product offerings such as Exalytics and Oracle Business Intelligence Enterprise Edition integrations; topics relevant to many existing implementations; and even a representation of partner products."—Gabby Rubin, Senior Director, Product Management, Oracle Business AnalyticsTable of ContentsIntroduction. Essbase on Exalytics and the "Secret Sauce". Hybrid Essbase: Evolution or Revolution? The Young Person’s Guide to Essbase Cube Design. Essbase Performance and Load Testing. Utilizing Structured Query Language to Enhance Your Essbase Experience. Copernicus Was Right: Integrating Oracle Business Intelligence and Essbase. Managing Spreadsheets (and Essbase) Through Dodeca. Smart View Your Way.

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Book SynopsisExploratory Data Analysis Using R provides a classroom-tested introduction to exploratory data analysis (EDA) and introduces the range of interesting - good, bad, and ugly - features that can be found in data, and why it is important to find them. It also introduces the mechanics of using R to explore and explain data. The book begins with a detailed overview of data, exploratory analysis, and R, as well as graphics in R. It then explores working with external data, linear regression models, and crafting data stories. The second part of the book focuses on developing R programs, including good programming practices and examples, working with text data, and general predictive models. The book ends with a chapter on keeping it all together that includes managing the R installation, managing files, documenting, and an introduction to reproducible computing. The book is designed for both advanced undergraduate, entry-level graduate students, and working professionals with little to no

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Book SynopsisBiometrics in a Data Driven World: Trends, Technologies, and Challenges aims to inform readers about the modern applications of biometrics in the context of a data-driven society, to familiarize them with the rich history of biometrics, and to provide them with a glimpse into the future of biometrics.The first section of the book discusses the fundamentals of biometrics and provides an overview of common biometric modalities, namely face, fingerprints, iris, and voice. It also discusses the history of the field, and provides an overview of emerging trends and opportunities. The second section of the book introduces readers to a wide range of biometric applications. The next part of the book is dedicated to the discussion of case studies of biometric modalities currently used on mobile applications. As smartphones and tablet computers are rapidly becoming the dominant consumer computer platforms, biometrics-based authentication is emergingTable of ContentsIntroduction to Biometric Authentication. Challenges in Biometric Systems. Emerging trends and new opportunities in Biometrics. Biometrics in the mobile world. Biometric authentication techniques in the mobile platform. Case Studies of real-world mobile biometric systems. Biometrics in wearable technology and healthcare applications. Biometrics in Social Networks. Biometrics in Gaming Technologies. Biometrics in homeland security. Computational issues in biometrics. New directions in Biometrics research: What does the future hold?

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Book SynopsisData Stewardship for Open Science: Implementing FAIR Principles has been written with the intention of making scientists, funders, and innovators in all disciplines and stages of their professional activities broadly aware of the need, complexity, and challenges associated with open science, modern science communication, and data stewardship. The FAIR principles are used as a guide throughout the text, and this book should leave experimentalists consciously incompetent about data stewardship and motivated to respect data stewards as representatives of a new profession, while possibly motivating others to consider a career in the field. The ebook, avalable for no additional cost when you buy the paperback, will be updated every 6 months on average (providing that significant updates are needed or avaialble). Readers will have the opportunity to contribute material towards these updates, and to develop their own data management plans, via the free Data Stewardship Wizard.

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Book SynopsisThe proposed book talks about the participation of human in Big Data.How human as a component of system can help in making the decision process easier and vibrant.It studies the basic build structure for big data and also includes advanced research topics.In the field of Biological sciences, it comprises genomic and proteomic data also. The book swaps traditional data management techniques with more robust and vibrant methodologies that focus on current requirement and demand through human computer interfacing in order to cope up with present business demand. Overall, the book is divided in to five parts where each part contains 4-5 chapters on versatile domain with human side of Big Data.Table of ContentsPrefaceEditorsContributorsSection I Introduction to the Human Element of Big Data: Definition, New Trends, and Methodologies1 Taming the Realm of Big Data Analytics: Acclamation or Disaffection?Audrey Depeige2 Fast Data Analytics Stack for Big Data AnalyticsSourav Mazumder3 Analytical Approach for Big Data in the Internet of ThingsAnand Paul, Awais Ahmad, and M. Mazhar Rathore4 Analysis of Costing Issues in Big DataKuldeep Singh Jadon and Radhakishan YadavSection II Algorithms and Applications of Advancement in Big Data5 An Analysis of Algorithmic Capability and Organizational ImpactGeorge Papachristos and Scott W. Cunningham6 Big Data and Its Impact on Enterprise ArchitectureMeena Jha, Sanjay Jha, and Liam O’Brien7 Supportive Architectural Analysis for Big DataUtkarsh Sharma and Robin Singh Bhadoria8 Clustering Algorithms for Big Data: A SurveyAnkita Sinha and Prasanta K. JanaSection III Future Research and Scope for the Human Element of Big Data9 Smart Everything: Opportunities, Challenges, and ImpactSiddhartha Duggirala10 Social Media and Big DataRichard Millham and Surendra Thakur11 Big Data Integration, Privacy, and SecurityRafael Souza and Chandrakant Patil12 Paradigm Shifts from E-Governance to S-GovernanceAkshi Kumar and Abhilasha SharmaSection IV Case Studies for the Human Element of Big Data: Analytics and Performance13 Interactive Visual Analysis of Traffic Big DataZhihan Lv, Xiaoming Li, Weixi Wang, Jinxing Hu, and Ling Yin14 Prospect of Big Data Technologies in HealthcareRaghavendra Kankanady and Marilyn Wells15 Big Data Suite for Market Prediction and Reducing Complexity Using Bloom FilterMayank Bhushan, Apoorva Gupta, and Sumit Kumar Yadav16 Big Data Architecture for Climate Change and Disease DynamicsDaphne Lopez and Gunasekaran ManogaranIndex

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Book SynopsisData Mining: A Tutorial-Based Primer, Second Edition provides a comprehensive introduction to data mining with a focus on model building and testing, as well as on interpreting and validating results. The text guides students to understand how data mining can be employed to solve real problems and recognize whether a data mining solution is a feasible alternative for a specific problem. Fundamental data mining strategies, techniques, and evaluation methods are presented and implemented with the help of two well-known software tools. Several new topics have been added to the second edition including an introduction to Big Data and data analytics, ROC curves, Pareto lift charts, methods for handling large-sized, streaming and imbalanced data, support vector machines, and extended coverage of textual data mining. The second edition contains tutorials for attribute selection, dealing with imbalanced data, outlier analysis, time series analysis, mining textual data, and morTrade Review"Dr. Roiger does an excellent job of describing in step by step detail formulae involved in various data mining algorithms, along with illustrations. In addition, his tutorials in Weka software provide excellent grounding for students in comprehending the underpinnings of Machine Learning as applied to Data Mining. The inclusion of RapidMiner software tutorials and examples in the book is also a definite plus since it is one of the most popular Data Mining software platforms in use today."--Robert Hughes, Golden Gate University, San Francisco, CA, USATable of ContentsData Mining: A First View. Data Mining: A Closer Look. Basic Data Mining Techniques. Weka – A Tool for Knowledge Discovery.Pre Processing & Visualization Techniques. Knowledge Discovery in Databases. Formal Evaluation Techniques. The DataWarehouse. Neural Networks. Building Neural Networks with BpKNet. Statistical Methods. Specialized Techniques. A Case Studyin Knowledge Discovery. Rule-Based Systems. Managing Uncertainty in Rule-Based Systems. Intelligent Agents

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Book SynopsisFrom the Foreword:Big Data Management and Processing is [a] state-of-the-art book that deals with a wide range of topical themes in the field of Big Data. The book, which probes many issues related to this exciting and rapidly growing field, covers processing, management, analytics, and applications... [It] is a very valuable addition to the literature. It will serve as a source of up-to-date research in this continuously developing area. The book also provides an opportunity for researchers to explore the use of advanced computing technologies and their impact on enhancing our capabilities to conduct more sophisticated studies.---Sartaj Sahni, University of Florida, USABig Data Management and Processing covers the latest Big Data research results in processing, analytics, management and applications. Both fundamental insights and representative applications are provided. This book is a timely and valuable resource for students, researchers and seaTable of ContentsBig Data Management. Big Data Design, implementation, evaluation and services. Big Data as integration of technologies. Big Data analytics and visualization. Query processing and indexing. Elasticity for data management systems. Self-adaptive and energy-efficient mechanisms. Performance evaluation. Security, privacy, trust, data ownership and risk simulations. Processing. Techniques, algorithms and innovative methods of processing. Business and economic models. Adoption cases, frameworks and user evaluations. Data-intensive and scalable computing on hybrid infrastructures. MapReduce based computations. Many-Task Computing in the Cloud. Streaming and real-time processing. Big Data systems and applications for multidisciplinary applications.

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Book SynopsisThis book is about the process of using analytics and the capabilities of analytics in today's organizations. Cutting through the buzz surrounding the term analytics and the overloaded expectations about using analytics, the book demystifies analytics with an in-depth examination of concepts grounded in operations research and management science. Analytics as a set of tools and processes is only as effective as: The data with which it is working The human judgment applying the processes and understanding the output of these processes. For this reason, the book focuses on the analytics process. What is intrinsic to analytics' real organizational impact are the careful application of tools and the thoughtful application of their outcomes. This work emphasizes analytics as part of a process that supports decision-making within organizations. It wants to debunk overblown expectations that somehow analytics outputs or analytics as applied toTable of ContentsSECTION I. ANALYTICS PROCESS CONCEPTS. About the Analytics Process. Illustrating the Analytics Process through Risk Assessment. and Modeling. Analytics, Strategy, and Management Control Systems. SECTION II. ANALYTICS PROCESS APPLICATIONS. Data, Information, and Intelligence. The Rise of Big Data and Analytics in Higher Education. Google Analytics as a Prosumption Tool for Web Analytics. Knowledge-Based Cause–Effect Analysis Enriched by Generating Multilayered DSS Models. Online Community Projects in Lithuania: Cyber Security Perspective. Exploring Analytics in Health Information Delivery to Acute Health Care in Australia. Information Visualization and Knowledge Reconstruction of RFID Technology Translation in Australian Hospitals. Health Care Analytics and Big Data Management in Influenza Vaccination Programs: Use of Information–Entropy Approach. Sharing Knowledge or Just Sharing Data? Index.

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Book SynopsisThis is an edited book based on the selected submissions made to the conference titled International Conference in Smart Cities. The project provides an innovative and new approach to holistic management of cities physical, socio-economic, environmental, transportation and political assets across all domains, typically supported by ICT and open data.Table of ContentsAdoption and Acceptance of Mandatory Electronic Public Services by Citizens in the Developing World. Self-Sustainable Integrated Township. Smart People for Smart Cities. How Smart Cities influence Governance? Role of Manufacturing Sector to Develop Smart Economy. Concept of Smart Village in India. Smart City. Smart City Technologies. A Cloud-Based Mobile Application for Cashless Payments. Financial Viability of Energy Conservation using Natural Light. Information Risk for Digital Services. Mobile Commerce Research for Individual, Business and Society. The Shift Toward a Sustainable Urban Mobility through Decision Support Systems.

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Book SynopsisDESCRIPTIONEvent Streams in Action is a foundational book introducing the ULPparadigm and presenting techniques to use it effectively in data-richenvironments. The book begins with an architectural overview,illustrating how ULP addresses the thorny issues associated withprocessing data from multiple sources. It then guides the readerthrough examples using the unified log technologies Apache Kafkaand Amazon Kinesis and a variety of stream processing frameworksand analytics databases. Readers learn to aggregate events frommultiple sources, store them in a unified log, and build data processingapplications on the resulting event streams. As readers progressthrough the book, they learn how to validate, filter, enrich, and storeevent streams, master key stream processing approaches, and exploreimportant patterns like the lambda architecture, stream aggregation,and event re-processing. The book also dives into the methods andtools usable for event modelling and event analytics, along withscaling, resiliency, and advanced stream patterns. KEY FEATURES • Building data-driven applications that are easier to design,deploy, and maintain• Uses real-world examples and techniques• Full of figures and diagrams• Hands-on code samples and walkthroughs This book assumes that the reader has written some Java code. SomeScala or Python experience is helpful but not required. ABOUT THE TECHNOLOGYUnified Log Processing is a coherent data processing architecture thatcombines batch and near-real time stream data, event logging andaggregation, and data processing into a unified event stream. By efficientlycreating a single log of events from multiple data sources, Unified LogProcessing makes it possible to design large-scale data-driven applicationsthat are easier to design, deploy, and maintain. AUTHOR BIOAlexander Dean is co-founder and technical lead of Snowplow Analytics,an open source event processing and analytics platform.

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Book Synopsis Imagine predicting which customers are thinking about switching to a competitor or flagging potential process failures before they happen Think about the benefits of automating tedious business processes and back-office tasks Consider the competitive advantage of making decisions when you know the most likely future events Machine learning can deliver these and other advantages to your business, and it’s never been easier to get started! Machine Learning for Business teaches you how to make your company more automated, productive, and competitive by mastering practical, implementable machine learning techniques and tools. Thanks to the authors’ down-to-earth style, you’ll easily grok why process automation is so important and why machine learning is key to its success. In this hands-on guide, you’ll work through seven end-to-end automation scenarios covering business processes in accounts payable, billing, payroll, customer support, and other common tasks. Using Amazon SageMaker (no installation required!), you’ll build and deploy machine learning applications as you practice takeaway skills you’ll use over and over. By the time you’re finished, you’ll confidently identify machine learning opportunities in your company and implement automated applications that can sharpen your competitive edge! Key Features Identifying processes suited to machine learning Using machine learning to automate back office processes Seven everyday business process projects Using open source and cloud-based tools Case studies for machine learning decision making For technically-inclined business professionals or business developers. No previous experience with automation tools or programming is necessary. Doug Hudgeon runs a business automation consultancy, putting his considerable experience helping companies set up automation and machine learning teams to good use. In 2000, Doug launched one of Australia’s first electronic invoicing automation companies. Richard Nichol has over 20 years of experience as a data scientist and software engineer. He currently specializes in maximizing the value of data through AI and machine learning techniques.

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Book Synopsis

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Book SynopsisWithin a healthcare enterprise, patient vital signs and other automated measurements are communicated from connected medical devices to end-point systems, such as electronic health records, data warehouses and standalone clinical information systems. Connected Medical Devices: Integrating Patient Care Data in Healthcare Systems explores how medical device integration (MDI) supports quality patient care and better clinical outcomes by reducing clinical documentation transcription errors, improving data accuracy and density within clinical records and ensuring the complete capture of medical device information on patients. The book begins with a comprehensive overview of the types of medical devices in use today and the ways in which those devices interact, before examining factors such as interoperability standards, patient identification, clinical alerts and regulatory and security considerations. Offering lessons learned from his own experiences managing MDI rollouts in both operating room and intensive care unit settings, the author provides practical guidance for healthcare stakeholders charged with leading an MDI rollout. Topics include working with MDI solution providers, assembling an implementation team and transitioning to go-live. Special features in the book include a glossary of acronyms used throughout the book and sample medical device planning and testing tools.Table of ContentsIntroduction: Overview of Medical Device Interoperability (MDI) and the Current State of the MDI Industry, What is MDI and Why is it Needed Chapter 1: Medical Device Types and Classes Used and How They Communicate Chapter 2: MDI Solution Acquisition and Implementation Chapter 3: Semantic Data Alignment and Time Synchronization of Medical Devices Chapter 4: Standards Surrounding Medical Device Integration To Health Systems Chapter 5: Notifications, Alerts and Clinical Uses of Medical Device Data Chapter 6: Patient Identification and Medical Device Association Chapter 7: Regulatory and Security Considerations of MDI

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Book SynopsisThis book is about the design and development of tools for software testing. It intends to get the reader involved in software testing rather than simply memorizing the concepts. The source codes are downloadable from the book website. The book has three parts: software testability, fault localization, and test data generation. Part I describes unit and acceptance tests and proposes a new method called testability-driven development (TsDD) in support of TDD and BDD. TsDD uses a machine learning model to measure testability before and after refactoring. The reader will learn how to develop the testability prediction model and write software tools for automatic refactoring. Part II focuses on developing tools for automatic fault localization. This part shows the reader how to use a compiler generator to instrument source code, create control flow graphs, identify prime paths, and slice the source code. On top of these tools, a software tool, Diagnoser, is offered to facilitate experimenting with and developing new fault localization algorithms. Diagnoser takes a source code and its test suite as input and reports the coverage provided by the test cases and the suspiciousness score for each statement. Part III proposes using software testing as a prominent part of the cyber-physical system software to uncover and model unknown physical behaviors and the underlying physical rules. The reader will get insights into developing software tools to generate white box test data.

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Book SynopsisAdvanced Guide to Python 3 Programming 2nd Edition delves deeply into a host of subjects that you need to understand if you are to develop sophisticated real-world programs. Each topic is preceded by an introduction followed by more advanced topics, along with numerous examples, that take you to an advanced level.This second edition has been significantly updated with two new sections on advanced Python language concepts and data analytics and machine learning. The GUI chapters have been rewritten to use the Tkinter UI library and a chapter on performance monitoring and profiling has been added. In total there are 18 new chapters, and all remaining chapters have been updated for the latest version of Python as well as for any of the libraries they use. There are eleven sections within the book covering Python Language Concepts, Computer Graphics (including GUIs), Games, Testing, File Input and Output, Databases Access, Logging, Concurrency and Parallelism, Reactive Programming, Networking and Data Analytics. Each section is self-contained and can either be read on its own or as part of the book as a whole. It is aimed at those who have learnt the basics of the Python 3 language but wish to delve deeper into Python’s eco system of additional libraries and modules.Table of ContentsIntroduction.- Part 1: Advanced language features.- Python type hints.- Class slots.- Weak references.- Data classes.- Structural pattern matching.- Working with pprint.- Shallow v deep copy.- The __init__versus __new__ and __call__.- Python metaclasses and meta programming.- Part 2: Computer graphics and GUIs.- Introduction to computer graphics.- Python turtle graphics.- Computer generated art.- Introduction to Matplotlib.- Graphing with Matplotlib pyplot.- Graphical user interfaces.- Tkinter GUI library.- Events in Tkinter user interfaces.- PyDraw Tkinter example application.- Part 3: Computer graphics and GUIs.- Introduction to games programming.- Building games with pygame.- StarshipMeteors pygame.- Part 4: Testing.- Introduction to testing.- PyTest testing framework.- Mocking for testing.- Part 5: File Input / Output.- Introduction to files, paths and IO.- Reading and writing files.- Stream IO.- Working with CSV files.- Working with excel files.- Regular expressions in Python.- Part 6: Database access.- Introduction to databases.- Python DB-API.- PyMySQL module.- Part 7: Logging.- Introduction to logging.- Logging in Python.- Advanced logging.- Part 8: Concurrency and parallelism.- Introduction to concurrency and parallelism.- Threading.- MultiProcessing.- Inter thread / Process synchronisation.- Futures.- Concurrency with AsyncIO.- Performance monitoring and profiling.- Part 9: Reactive programming.- Reactive programming introduction.- RxPy observables, observers and subjects.- RxPy operators.- Part 10: Network programming.- Introduction to sockets and web services.- Sockets in Python.- Web services in Python.- Flask web services.- Flask bookshop web service.- Part 11: Data analytics and machine learning.- Introduction to data science.- Pandas and data analytics.- Alternatives to pandas.- Machine learning in Python.- Pip and Conda virtual environments.

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Book SynopsisDeep Convolutional Neural Networks (CNNs).- Deep CNNs for Image Classification, Object Detection, and Segmentation.- Attention and Transformer Networks.- Transformer-based Approaches for Medical Image Analysis.- Deep Learning Networks for 3D Medical Image Analysis.- Multimodal Deep Learning for Medical Image Analysis.- Semi-supervised Learning for Medical Image Analysis.- Domain Adaptation and Generalization for Medical Image Analysis.- Deep Learning Models for Medical Image Translation.- Foundation Models for Medical Image Analysis.

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Book Synopsis.- Industrial Keynote..- From Data Silos to Data Mesh: A Case Study in Financial Data Architecture..- Invited Talk..- Blending Contextual Data with Heterogeneous Time Dimensions for Improved Time Series Analysis..- A Hybrid Data Model to Support Transportation Analytics of Emergency Service Vehicles..- Large Language Models..- Automated Archival Descriptions with Federated Intelligence of LLMs..- Entropy-Guided Probing for Predicting LLM Hallucinations with Knowledge Graph Features..- Towards Automating RDF Extraction for Archaeological Knowledge Graphs with LLMs..- Ontology-Based Forest Fire Management using Complex Event Processing and Large Language Models..- Table Annotation Utilizing Large Language Model and Knowledge Graph..- Improving Software Security Through a LLM-Based Vulnerability Detection Model..- SysResolve: Study on In-Context LLM Generation of Resolution Scripts..- Data Quality..- A Novel Unsupervised Anomaly Detection Method Based on TCN-LSTM-CMA Autoencoder..- Behaviour modelling and Wayfinding Error Detection in Low Mountain Hiking..- Explainable Time Series Anomaly Detection by Dynamic Mode Decomposition..- Exploring Quantum Bootstrap Sampling for AQP Error Assessment: A Pilot Study..- AI-Driven Semantic Data Quality Assessment and Scoring for Relational Databases..- Network Anomaly Detection Using Gramian Angular Field Transformation and Vision Transformer..- Machine Learning /Artificial Intelligence Applications..- Identifying Multimodal Sarcasm Based on Incongruous Knowledge Capturing and Contrastive Learning..- Ensemble ToT and Its Application to Automatic Grading..- Improving Prompt-based Learning Framework for Mental Health Aspect Detection from Social Media..- DInos: A Deep Reinforcement Learning Approach to Generalizable Autoscaling in Stateless Cloud Applications..- Influential Slot and Tag Selection in Billboard Advertisement..- Speech-scenario Generation based on the Philosophy of Prominent Leader within the Small Community..- VarCGAN: Variational Cyclic Generative Adversarial Network for Music Genre Style Transfer..- Innovative Framework for Early Estimation of Mental Disorder Scores to Enable Timely Interventions..- A Hybrid Approach to estimating AI Carbon Emissions..- Optimal Information Retrieval System in E-Learning Using Optimization-Driven Bidirectional Long Short-Term Memory..- A Data Product Classification by Technical and Machine Learning Aspects..- Classification Techniques..- Discovering Voting Power for Ensemble Methods..- Classifying Public and Private Documents Using Context-Based Predictions.

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Book Synopsis.- Image Processing, Analytics, and Vision Systems..- Relationship Analysis of Image-Text Pair in SNS Posts..- Enhancing Segmentation of Irregular Microstructural Elements Using Extended Channel Information and Transfer Learning..- Deep-RVT: A Residual Vision Transformers for Human Action Recognition..- Recommender Techniques..- Food Recommendation with Balancing Comfort and Curiosity..- ONFOOD: A Substitute Recommendation System in Food Recipes..- Inspire Me with Your Questions: Repurposing Historical Questions for New Documents..- Data Integration..- MRF-JOIN: Differentially Private Vertical Data Synthesis via Federated Marginal Join on Shared Attributes..- Efficient Source Selection for Federated SPARQL Queries using Adjacent Predicate Information..- Empathetic Response Generation in Emotional Support Conversation via Multi- Stage Cascading Information Fusion..- Unified Schema-Driven Graph Polystore: Achieving Transparency in Multi-Model Integration and Migration..- Optimisation Methods..- Group Trip Planning Query Problem with Multimodal Journey..- A Model-based Approach for Simple Construction and Efficient Evaluation of Dataframes..- Energy and Performance Evaluation of Serverless and Serverful Models on Spark for Database Join Operations..- Graph Applications..- The Missing Link: Joint Legal Citation Prediction using Heterogeneous Graph Enrichment..- Graph Patterns in Fine-grained Access Control for Graph-structured Data..- An Efficient Point-of-Interest Placement Method Based on Betweenness Centrality..- Analytics..- Analytics Modelling over Multiple Datasets using Vector Embeddings..- Towards IoT-based Smart Mobility Framework for Proactive Road Stress Detection in Individuals with ASD..- A Divisive Unsupervised Feature Selection Approach for Explainable Remaining Useful Life Prediction..- Data Storytelling to Unlock the Communicative Power of Digital Twins..- Queueing Theory for Verifying the Utilization Rate of an Image Processing System..- Effect of frequency features of ELA maps on the detection performance of image manipulation based on DCT and FFT basis features..- Alpha: A Multi-Attention Enhanced YOLO Framework for Robust Photovoltaic Defect Detection..- Security/Privacy..- Secure Approach for Blockchain-based Anonymous Attribute-based Searchable Encryption Scheme for Data Sharing..- Incremental k-anonymization for Continuously Growing Big Databases..- Post Quantum Cryptographic Schemes and Libraries Selection..- Benchmarks and Surveys..- Workload-Based Clustering of Large Number of Database-as-a-Service Instances..- Accelerating Python Code with Parallel I/O..- Benchmarking Embedding Techniques for Modeling User Navigation Behavior on Task-Oriented Software..- The Wrecking SQL Incremental Validation Methodology..- A Survey of Control Technologies for Autonomous Underwater Vehicles.

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Book SynopsisAlgorithms are at the heart of every nontrivial computer application, and algorithmics is a modern and active area of computer science. Every computer scientist and every professional programmer should know about the basic algorithmic toolbox: structures that allow efficient organization and retrieval of data, frequently used algorithms, and basic techniques for modeling, understanding and solving algorithmic problems. This book is a concise introduction addressed to students and professionals familiar with programming and basic mathematical language. Individual chapters cover arrays and linked lists, hash tables and associative arrays, sorting and selection, priority queues, sorted sequences, graph representation, graph traversal, shortest paths, minimum spanning trees, and optimization. The algorithms are presented in a modern way, with explicitly formulated invariants, and comment on recent trends such as algorithm engineering, memory hierarchies, algorithm libraries and certifying algorithms. The authors use pictures, words and high-level pseudocode to explain the algorithms, and then they present more detail on efficient implementations using real programming languages like C++ and Java. The authors have extensive experience teaching these subjects to undergraduates and graduates, and they offer a clear presentation, with examples, pictures, informal explanations, exercises, and some linkage to the real world. Most chapters have the same basic structure: a motivation for the problem, comments on the most important applications, and then simple solutions presented as informally as possible and as formally as necessary. For the more advanced issues, this approach leads to a more mathematical treatment, including some theorems and proofs. Finally, each chapter concludes with a section on further findings, providing views on the state of research, generalizations and advanced solutions.Trade Review"This is another mainstream textbook on algorithms and data structures, mainly intended for undergraduate students and professionals … . The two-layer index table is also detailed and helpful. I do enjoy reading the informative sections of historical notes and further findings at the end of each chapter. … This book is very well written, with the help of … clear figures and tables, as well as many interesting and inspiring examples." Zhizhang Shen, Zentralblatt MATH, Vol. 1146, 2008"... the book develops the basic fundamental principles underlying their design and analysis without sacrificing depth or rigor. The authors' insight, knowledge and active research on algorithms and data structures provide a very solid approach to the book. I particularly liked their "as informally as possible and as formally as necessary" writing style, and I enjoyed a lot their decision to not only discuss classical results, but to broaden the view to alternative implementations, memory hierarchies and libraries, which transmits novelty and increases interest...I think that this book will be a superb addition particularly useful for teachers of undergraduate courses, to graduate students in Computer Science, and to researchers that work, or intend to work, with algorithms." Jordi Petit, Computer Science Review 3, 2009 "Mehlhorn and Sanders write well, and the well-organized presentation reflects their experience and interest in the various topics... it is an excellent reference, and could possibly be used in a transition course, serving students coming to graduate CS courses from other technical fields. [...]This text is intended for undergraduate computer science (CS) majors, and focuses on algorithm analysis. … it is an excellent reference, and could possibly be used in a transition course, serving students coming to graduate CS courses from other technical fields. Finally, the book contains interesting tidbits that are not readily available elsewhere." M. G. Murphy, ACM Computing Reviews, October 2008"A 'Toolbox' should be portable, practical, and useful. This book is all these, covering a nice swath of the classic CS algorithms but addressing them in a way that is accessible to the student and practitioner. Furthermore, it manages to incorporate interesting examples as well as subtle examples of wit compressed into its 300 pages. Although it is not tied to any one language or library, it provides practical references to efficient open-source implementations of many of the algorithms and data structures; these should be the first refuge of the commercial developer. I can easily recommend this book as an intermediate undergraduate text, a refresher for those of us who only dimly remember our intermediate undergraduate courses, and as a reference for the professional development craftsman." Hal C. Elrod, SIGACT News Book Review Column 42(4) 2011Table of ContentsAppetizer: Integer Arithmetics.- Representing Sequences by Arrays and Linked Lists.- Hash Tables and Associative Arrays.- Sorting and Selection.- Priority Queues.- Sorted Sequences.- Graph Representation.- Graph Traversal.- Shortest Paths.- Minimum Spanning Trees.- Generic Approaches to Optimization.

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Book SynopsisThe design and analysis of data structures and efficient algorithms has gained considerable importance in recent years. The concept of "algorithm" is central in computer science, and "efficiency" is central in the world of money. I have organized the material in three volumes and nine chapters. Vol. 1: Sorting and Searching (chapters I to III) Vol. 2: Graph Algorithms and NP-completeness (chapters IV to VI) Vol. 3: Multi-dimensional Searching and Computational G- metry (chapters VII and VIII) Volumes 2 and 3 have volume 1 as a common basis but are indepen­ dent from each other. Most of volumes 2 and 3 can be understood without knowing volume 1 in detail. A general kowledge of algorith­ mic principles as laid out in chapter 1 or in many other books on algorithms and data structures suffices for most parts of volumes 2 and 3. The specific prerequisites for volumes 2 and 3 are listed in the prefaces to these volumes. In all three volumes we present and analyse many important efficient algorithms for the fundamental computa­ tional problems in the area. Efficiency is measured by the running time on a realistic model of a computing machine which we present in chapter I. Most of the algorithms presented are very recent inven­ tions; after all computer science is a very young field. There are hardly any theorems in this book which are older than 20 years and at least fifty percent of the material is younger than 10 years.Table of ContentsI. Foundations.- 1. Machine Models: RAM and RASP.- 2. Randomized Computations.- 3. A High Level Programming Language.- 4. Structured Data Types.- 4.1 Queues and Stacks.- 4.2 Lists.- 4.3 Trees.- 5. Recursion.- 6. Order of Growth.- 7. Secondary Storage.- 8. Exercises.- 9. Bibliographic Notes.- II. Sorting.- 1. General Sorting Methods.- 1.1 Sorting by Selection, a First Attempt.- 1.2 Sorting by Selection: Heapsort.- 1.3 Sorting by Partitioning: Quicksort.- 1.4 Sorting by Merging.- 1.5 Comparing Different Algorithms.- 1.6 Lower Bounds.- 2. Sorting by Distribution.- 2.1 Sorting Words.- 2.2 Sorting Reals by Distribution.- 3. The Lower Bound on Sorting, Revisited.- 4. The Linear Median Algorithm.- 5. Exercises.- 6. Bibliographic Notes.- III. Sets.- 1. Digital Search Trees.- 1.1 Tries.- 1.2 Static Tries or Compressing Sparse Tables.- 2. Hashing.- 2.1 Hashing with Chaining.- 2.2 Hashing with Open Addressing.- 2.3 Perfect Hashing.- 2.4 Universal Hashing.- 2.5 Extendible Hashing.- 3. Searching Ordered Sets.- 3.1 Binary Search and Search Trees.- 3.2 Interpolation Search.- 4. Weighted Trees.- 4.1 Optimum Weighted Trees, Dynamic Programming, and Pattern Matching.- 4.2 Nearly Optimal Binary Search Trees.- 5. Balanced Trees.- 5.1 Weight-Balanced Trees.- 5.2 Height-Balanced Trees.- 5.3 AdvancedTopicson(a,b)-Trees.- 5.3.1 Mergable Priority Queues.- 5.3.2 Amortized Rebalancing Cost and Sorting Presorted Files.- 5.3.3 Finger Trees.- 5.3.4 Fringe Analysis.- 6. Dynamic Weighted Trees.- 6.1 Self-Organizing Data Structures and Their Amortized and Average Case Analysis.- 6.1.1 Self-Organizing Linear Lists.- 6.1.2 Splay Trees.- 6.2 D-trees.- 6.3 An Application to Multidimensional Searching.- 7. A Comparison of Search Structures.- 8. Subsets of a Small Universe.- 8.1 The Boolean Array (Bitvector).- 8.2 The O(log log N) Priority Queue.- 8.3 The Union-Find Problem.- 9. Exercises.- 10. Bibliographic Notes.- IX. Algorithmic Paradigms.

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Table of ContentsVII. Multidimensional Data Structures.- 1. A Black Box Approach to Data Structures.- 1.1 Dynamization.- 1.2 Weighting and Weighted Dynamization.- 1.3 Order Decomposable Problems.- 2. Multi-dimensional Searching Problems.- 2.1 D-dimensional Trees and Polygon Trees.- 2.2 Range Trees and Multidimensional Divide and Conquer.- 2.3 Lower Bounds.- 2.3.1 Partial MatchRetrieval in Minimum Space.- 2.3.2 The Spanning Bound.- 3. Exercises.- 4. Bibliographic Notes.- VIII. Computational Geometry.- 1. Convex Polygons.- 2. Convex Hulls.- 3. Voronoi Diagrams and Searching Planar Subdivisions.- 3.1 Voronoi Diagrams.- 3.2 Searching Planar Subdivisions.- 3.2.1 Removal of Large Independent Sets.- 3.2.2 Path Decompositions.- 3.2.3 Searching Dynamic Planar Subdivisions.- 3.3 Applications.- 4. The Sweep Paradigm.- 4.1 Intersection of Line Segments and Other Intersection Problems in the Plane.- 4.2 Triangulation and its Applications.- 4.3 Space Sweep.- 5. The Realm of Orthogonal Objects.- 5.1 Plane Sweep for Iso-Oriented Objects.- 5.1.1 The Interval Tree and its Applications.- 5.1.2 The Priority Search Tree and its Applications.- 5.1.3 Segment Trees.- 5.1.4 Path Decomposition and Plane Sweep for Non-Iso-Oriented Objects.- 5.2 Divide and Conquer on Iso-Oriented Objects.- 5.2.1 The Line Segment Intersection Problem.- 5.2.2 The Measure and Contour Problems.- 5.3 Intersection Problems in Higher-Dimensional Space.- 6. Geometric Transforms.- 6.1 Duality.- 6.2 Inversion.- 7. Exercises.- 8. Bibliographic Notes.- IX. Algorithmic Paradigms.

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Book SynopsisThis book presents the refereed proceedings of the 5th International Conference on Advanced Machine Learning Technologies and Applications (AMLTA 2020), held at Manipal University Jaipur, India, on February 13 – 15, 2020, and organized in collaboration with the Scientific Research Group in Egypt (SRGE). The papers cover current research in machine learning, big data, Internet of Things, biomedical engineering, fuzzy logic and security, as well as intelligence swarms and optimization. Table of ContentsSegregating and Recognizing Human Actions from Video Footages using LRCN Technique.- Fall Alert: A Novel Approach to Detect Fall.- Evaluation of Automatic Text Visualization Systems: A Case Study.- Face Recognition Based Attendance System using Real Time Computer Vision Algorithms.- The Impact of Knowledge Management Adoption on the Government Sector’s Performance: The Case of Bahrain.- Video Surveillance for the Crime Detection using Features.- Real-time Neural-net Driven Optimized Inverse-kinematics for a Robotic Manipulator.- A Deep Learning Technique to Countermeasure Video Based Presentation Attacks.- Optimization of Loss Functions for Predictive Soil Mapping.- Natural Language Information Extraction through Non Factoid Question and Answering System.- An Enhanced Differential Evolution Algorithm with New Environmental-based Parameters for Solving Optimization Problems.- Reactive Power Optimization Approach based on Chaotic Particle Swarm Optimization.- Data Mining Model for Better Admissions in Higher Educational Institutions (HEIs) – A Case Study of Bahrain.- The Effectiveness of Renewable Energies Projects in Kuwait - PAAET Solar Energy Project.- Skin Lesion Analyser: An Efficient Seven-Way Multi-Class Skin Cancer Classification Using Mobile Net.- Real-Time Object Detection in Remote Sensing Images using Deep Learning.- Malaria Detection using Convolutional Neural Network.- Drone-Based Face Recognition using Deep Learning.- Traffic Sign Recognition for Self-Driving Cars with Deep Learning.- Identifying the Association Rule to Determine the Possibilities of Cardio Vascular Diseases(CVD).- Prediction of Service Level Agreement Violation in Cloud Computing using Bayesian Regularization.- A New Methodology for Language Identification in Social Media Code-mixed Text.- Detecting Influencers in Social Networks Through Machine Learning Techniques.- Application and Analysis of K-Means Algorithms on a Decision Support Framework for Municipal Solid Waste Management.- Android Rogue Application Detection using Image Resemblance and Reduced LDA.- An Indexed Non-Probability Skyline Query Processing Framework for Uncertain Data.- Analysis of Operational Control Mode of Intelligent Distribution Grids with Multi-microgrid.- Technical Present Situation based on Micro Grid Operation Control.- Skin Lesion Classification: A Transfer Learning Approach Using Efficient Nets.- Change Footprint Pattern Analysis of Crime Hotspot of Indian Districts.- Itemset Mining based Episode Profiling of Terrorist Attacks using Weighted Ontology.- Enabling Technologies in Banking Industry, Regulatory Technology RegTech and Money Laundering Prevention.- A Cognitive Knowledge Base for Learning Disabilities using Concept Analysis.- Native Monkey Detection using Deep Convolution Neural Network.- Evaluation and Summarization of Student Feedbacks Using Sentiment Analysis.- Predicting Competitive Weight Lifting Performance using Regression and Tree-based Algorithms.- Predicting the Primary Dominant Personality Trait of Perceived Leaders by Mapping Linguistic Cues from Social Media Data onto the Big-Five Model.- Analysis of Users behaviour on Micro Blogging Site using a Topic.- Machine Learning Techniques for Short Term Forecasting of Wind Power Generation.- Integrated Process Management System of Smart Substation Secondary Side based on Practical Scheme.- Research on Forms and Strategies of Alternative Energy Achieved.- Lightweight Access Control Algorithm for Internet of Things.- Named Entity Recognition for Legal Documents.- Visual Speech Processing and Recognition.- Predictive Analytics for Cardiovascular Disease Diagnosis using Machine Learning Techniques.- A Novel Approach for Smart Health Care Recommender System.- Heart Disorder Prognosis employing KNN, ANN, ID3 and SVM.- IoT based Home Security System with Wireless Communication.- Implementation of Internet of Things IoT in Small Business Industry: Case of Bahrain.- A Comparative Study of Model-Free Reinforcement Learning Approaches.- Location Aware Security System for Smart Cities using IoT.- An Assessment Study of Gait Biometric Recognition using Machine Learning.- A Study on Risk Management Practices in Online Banking in Bahrain.- Deep Learning Techniques: An Overview.- A Multilayer Deep Learning Framework For Auto-Content Tagging.- Case Based Reasoning (CBR) based Anemia Severity Detection System (ASDS) Using Machine Learning Algorithm.- ECG Signal Analysis, Diagnosis and Transmission.- The Effect of Real-Time Feedback on Consumer’s Behaviour in the Energy Management Sector: Empirical study.- Synchronization Control in Fractional Discrete-Time Systems with Chaotic Hidden Attractors.- Employment of Cryptographic Modus Operandi based on Trigonometric Algorithm and Resistor Color Code.- Experimental & Dimensional Analysis Approach for Human Energy Required In Wood Chipping Process.- Impact of High-k gate dielectric and Workfunctions Variation on Electrical Characteristics of VeSFET.- Correlating Personality Traits to Different Aspects of Facebook Usage.- Fractional Order control of a Fuel Cell-boost Converter System.- Battery Pack Construction Scheme based on UPS System Reliability.- Study on Land Compensation for High Voltage Transmission Lines in Power Grid based on Easement.- Study on the Terrain of Power Network Construction Expropriation and Legal Risk Prevention.

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Book SynopsisThis book includes selected papers presented at World Conference on Information Systems for Business Management (ISBM 2022), held in Bangkok, Thailand, during September 2–3, 2022. It covers up-to-date cutting-edge research on data science, information systems, infrastructure and computational systems, engineering systems, business information systems, and smart secure systems.Table of Contents Social Media as Communication-Transformation Tools.- Bi directional DC-DC converter-based Energy Storage Method for Electric Vehicles.- Design of Smart Irrigation System in Sone Command Area Bihar for Paddy Crop.- A Footstep to Image Deconvolution Technique for the both Known and Unknown Blur Parameter.- Secured Monitoring of Unauthorized UAV by Surveillance Drone Using NS2.

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Book SynopsisThis book includes selected papers presented at the World Conference on Information Systems for Business Management (ISBM 2023), held in Bangkok, Thailand, on September 78, 2023.

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Book SynopsisChapter 1: Introduction.- Chapter 2: Framework Overview.- Chapter 3: Deep Dive.- Chapter 4: Examples - Files.- Chapter 5: Examples -  Databases.- Chapter 6: Examples - Python.- Chapter 7: Examples - APIs.- Chapter 8: Platforms.- Chapter 9: Pipelines.- Chapter 10: Analog.- Appendix A.

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Book SynopsisTrade Review"You might think that museums are for collecting and laboratories for experimenting. Bruno J. Strasser tracks the creation of a hybrid culture--a 'way of knowing' that was comparative and experimental at the same time. Molecular biologists used the protein sequences of very various species to crack the genetic code. From bacteria to blood and protein to DNA, this engaging book restores collecting to the experimentalist tradition and gives 'big data' biology the history it needs."--Nick Hopwood, author of Haeckel's Embryos: Images, Evolution, and Fraud "Amidst all the hype surrounding Big Data and the life sciences, Bruno J. Strasser uncovers the deep continuities of collecting and comparing that link the latest data banks to venerable natural history museums. This bold book rethinks the relationship between field, laboratory, and archive, with important implications for the ethos of open publication in science."--Lorraine Daston, Max Planck Institute for the History of Science "The long-contested line between experimental life sciences and those that collect, compare, and classify is once more unsettled. It is now accepted that comparative sciences are open to experiment and always have been. And Bruno J. Strasser now argues that the celebrated achievements of experimental biology have similarly depended on practices of collecting and curating. And not just in our own new world of digital databases, but historically: from when experimenters first thought to make collecting forever obsolete. Strasser supports his bold revision with case studies of a broad range of sciences, from taxonomy to serology, experimental and then molecular biology, and bio-informatics. In its historical depth and breadth this is a benchmark book; and for all who want to know how life sciences really work, it's a must read."--Robert E. Kohler, University of Pennsylvania "A masterful, groundbreaking work: Strasser explores collecting activities in multiple branches of biology and medicine across several centuries, covering the territory from natural historical specimens to blood and proteins, and on to DNA sequences and contemporary big-data biology. His book assesses issues of lasting salience, including control of the collections, access to specimens and data, modes of publication, and assignment of authorship and credit. Strasser contends that big-data biology is not a sharp departure from the past but a hybrid, a joining of the experimentalist-reductionist inquiries into model organisms with the practices of collectors who classified and characterized their specimens and compared them with others. Strasser's research is wide and deep, his prose lucidly informative, and his analysis subtle, discerning, and persuasive." --Daniel J. Kevles, Yale University "Collecting Experiments is an exciting and welcome addition to the historiography of the long-standing debates about the changing roles of experimentation and description in the life sciences. Rejecting the older notion of an impassable dichotomy, Bruno J. Strasser suggests that the rise of experimental approaches to biology in the nineteenth century did not eclipse the more descriptive work of natural history, but rather became a part of an overall 'way of knowing' that included both approaches. 'Big data, ' whether obtained by experimental or observational methods had to be analyzed in the same manner. Strasser has done a great service to clarify the historical relationship between these two methodologies. It is a must for all scholars in the history of biology."--Garland Allen, Washington University in Saint Louis

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Book SynopsisData warehousing is one of the hottest business topics, and there's more to understanding data warehousing technologies than you might think. Find out the basics of data warehousing and how it facilitates data mining and business intelligence with Data Warehousing For Dummies, 2nd Edition.Table of ContentsIntroduction 1 Part I: The Data Warehouse: Home for Your Data Assets 7 Chapter 1: What’s in a Data Warehouse? 9 Chapter 2: What Should You Expect from Your Data Warehouse? 25 Chapter 3: Have It Your Way: The Structure of a Data Warehouse 37 Chapter 4: Data Marts: Your Retail Data Outlet 59 Part II: Data Warehousing Technology 71 Chapter 5: Relational Databases and Data Warehousing 73 Chapter 6: Specialty Databases and Data Warehousing 85 Chapter 7: Stuck in the Middle with You: Data Warehousing Middleware 95 Part III: Business Intelligence and Data Warehousing 113 Chapter 8: An Intelligent Look at Business Intelligence 115 Chapter 9: Simple Database Querying and Reporting 125 Chapter 10: Business Analysis (OLAP) 135 Chapter 11: Data Mining: Hi-Ho, Hi-Ho, It’s Off to Mine We Go 149 Chapter 12: Dashboards and Scorecards 155 Part IV: Data Warehousing Projects: How to Do Them Right 163 Chapter 13: Data Warehousing and Other IT Projects: The Same but Different 165 Chapter 14: Building a Winning Data Warehousing Project Team 179 Chapter 15: You Need What? When? — Capturing Requirements 193 Chapter 16: Analyzing Data Sources 203 Chapter 17: Delivering the Goods 213 Chapter 18: User Testing, Feedback, and Acceptance 225 Part V: Data Warehousing: The Big Picture 231 Chapter 19: The Information Value Chain: Connecting Internal and External Data 233 Chapter 20: Data Warehousing Driving Quality and Integration 247 Chapter 21: The View from the Executive Boardroom 263 Chapter 22: Existing Sort-of Data Warehouses: Upgrade or Replace? 271 Chapter 23: Surviving in the Computer Industry (and Handling Vendors) 281 Chapter 24: Working with Data Warehousing Consultants 291 Part VI: Data Warehousing in the Not-Too-Distant Future 297 Chapter 25: Expanding Your Data Warehouse with Unstructured Data 299 Chapter 26: Agreeing to Disagree about Semantics 305 Chapter 27: Collaborative Business Intelligence 311 Part VII: The Part of Tens 317 Chapter 28: Ten Questions to Consider When You’re Selecting User Tools 319 Chapter 29: Ten Secrets to Managing Your Project Successfully 325 Chapter 30: Ten Sources of Up-to-Date Information about Data Warehousing 331 Chapter 31: Ten Mandatory Skills for a Data Warehousing Consultant 335 Chapter 32: Ten Signs of a Data Warehousing Project in Trouble 339 Chapter 33: Ten Signs of a Successful Data Warehousing Project 343 Chapter 34: Ten Subject Areas to Cover with Product Vendors 347 Index 351

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Book SynopsisLike many other organizing paradigms, smart data strategy isrevolutionary and essential to enterprise performance. SmartData explores smart data strategy to enhance enterpriseperformance. Smart Data provides valuable tools in business,like skills for better enterprise decision-making, enterpriseperformance, and agility towards change.Table of ContentsForeword. Preface. Acknowledgments. Introduction: A Comprehensive Overview. Predictive Management. IDEF Lexicon for Executives. Organization of This Book. Smart Data in Three Dimensions. Business Rule. Case Study: IT Capital Budgeting Using a Knapsack Problem. Case Study: Better Decision Making: Field Testing, Evaluation and Validation of a Web-Based MedWatch Decision Support System (MWDSS). Engineering an Ubiquitous Strategy for Catalyzing Enterprise Performance Optimization. What Smart Data Provides. References. 1 Context: The Case and Place for Smart Data Strategy. 1.1 Value of Data to the Enterprise. 1.2 Enterprise Performance Versus Enterprise Integration. 1.3 Current Problems and Deficiencies from Poor Data Strategy. 1.4 New Technologies. 1.5 Breaking from Tradition with Improved Results. References. 2 Elements: Smart Data and Smart Data Strategy. 2.1 Performance Outcomes and Attributes. 2.2 Policy and Business Rules. 2.3 Expectations: Managerial and Technical. 2.4 Capacity for Change and Improvement. 2.5 Iteration Versus Big Bang. References. 3 Barriers: Overcoming Hurdles and Reaching a New Performance Trajectory. 3.1 Barriers. 3.2 Overcoming Barriers. 3.3 Top–Down Strategy. 3.4 Balance of Consequences and Reinforcement. 3.5 Collaboration. 3.6 Enterprise Performance Optimization Process. 3.7 Enterprise Performance Optimization Architecture. 3.8 Scoping, Scheduling, Budgeting, and Project and Program Management. References. 4 Visionary Ideas: Technical Enablement. 4.1 Today’s Possibilities. 4.2 Calibrating Executive Expectations. 4.3 Five Years from Now. 4.4 Ten Years From Now. References. 5. CEO’s Smart Data Handbook. 5.1 Strategy. 5.2 Policy. 5.3 Organization. 5.4 Actions. 5.5 Timing. 5.6 Funding and Costing Variables. 5.7 Outcomes and Measurements. References. Index. Wiley Series in Systems Engineering and Management.

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Book SynopsisThis book demonstrates the role of coding in communication and data storage systems design, illustrating the correct use of codes and the selection of the right code parameters. Relevant decoding techniques and their implementation are discussed in detail, while emphasizing the fundamental concepts of coding theory with minimal mathematical tools.Table of ContentsThe Principles of Coding in Digital Communications. Convolutional Codes. Linear Block Codes. Cyclic Codes. Finite Field Arithmetic. BCH Codes. Reed Solomon Codes. Performance Calculations for Block Codes. Multistage Coding. Iterative Decoding. Index.

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