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The W3C XQuery 3.1 standard provides a tool to search, extract, and manipulate content, whether it's in XML, JSON or plain text. With this fully updated, in-depth tutorial, you’ll learn to program with this highly practical query language.Designed for query writers who have some knowledge of XML basics, but not necessarily advanced knowledge of XML-related technologies, this book is ideal as both a tutorial and a reference. You’ll find background information for namespaces, schemas, built-in types, and regular expressions that are relevant to writing XML queries.This second edition provides:A high-level overview and quick tour of XQueryNew chapters on higher-order functions, maps, arrays, and JSONA carefully paced tutorial that teaches XQuery without being bogged down by the detailsAdvanced concepts for taking advantage of modularity, namespaces, typing, and schemasGuidelines for working with specific types of data, such as numbers, strings, dates, URIs, maps and arraysXQuery’s implementation-specific features and its relationship to other standards including SQL and XSLTA complete alphabetical reference to the built-in functions, types, and error messages

The W3C XQuery 3.1 standard provides a tool to search, extract, and manipulate content, whether it's in XML, JSON or plain text. With this fully updated, in-depth tutorial, you’ll learn to program with this highly practical query language.Designed for query writers who have some knowledge of XML basics, but not necessarily advanced knowledge of XML-related technologies, this book is ideal as both a tutorial and a reference. You’ll find background information for namespaces, schemas, built-in types, and regular expressions that are relevant to writing XML queries.This second edition provides:A high-level overview and quick tour of XQueryNew chapters on higher-order functions, maps, arrays, and JSONA carefully paced tutorial that teaches XQuery without being bogged down by the detailsAdvanced concepts for taking advantage of modularity, namespaces, typing, and schemasGuidelines for working with specific types of data, such as numbers, strings, dates, URIs, maps and arraysXQuery’s implementation-specific features and its relationship to other standards including SQL and XSLTA complete alphabetical reference to the built-in functions, types, and error messages

As we have an increasing amount of information available in electronic formats, we need a way of dealing with and managing the volume of data we store and exchange. To help us do this we can use meta data, which is simply data about data. XML's extensibility and interoperability make it an ideal format for meta data that is part of a scheme of sharing data between multiple sources. As concepts such as "the semantic web" and web services move closer to reality, a knowledge of meta data and how to use it will be of huge advantage. There have been a number of recent developments in the field of XML meta data, and Professional XML Meta Data takes a look at some of the initiatives at the bleeding edge of the XML meta data world. In this book you will see how schemas, topic maps, RDF, and inferencing can be put to use in the field of data description, discovery, and exchange.

As we have an increasing amount of information available in electronic formats, we need a way of dealing with and managing the volume of data we store and exchange. To help us do this we can use meta data, which is simply data about data. XML's extensibility and interoperability make it an ideal format for meta data that is part of a scheme of sharing data between multiple sources. As concepts such as "the semantic web" and web services move closer to reality, a knowledge of meta data and how to use it will be of huge advantage. There have been a number of recent developments in the field of XML meta data, and Professional XML Meta Data takes a look at some of the initiatives at the bleeding edge of the XML meta data world. In this book you will see how schemas, topic maps, RDF, and inferencing can be put to use in the field of data description, discovery, and exchange.

This book reviews state-of-the-art methodologies and techniques for analyzing enormous quantities of raw data in high-dimensional data spaces, to extract new information for decision making. The goal of this book is to provide a single introductory source, organized in a systematic way, in which we could direct the readers in analysis of large data sets, through the explanation of basic concepts, models and methodologies developed in recent decades.   If you are an instructor or professor and would like to obtain instructor’s materials, please visit http://booksupport.wiley.com If you are an instructor or professor and would like to obtain a solutions manual, please send an email to: pressbooks@ieee.org  

The future of healthcare technologies, and what they mean for investors and entrepreneurs The healthcare technology revolution is just around the corner. And when it arrives, it will change and enrich our lives in ways we can only begin to imagine. Doctors will perform blood pressure readings via video chat and nutritionists will analyze diet based on photos taken with cellphone cameras. Transforming Health Care combines healthcare, technology, and finance in an innovative new way that explains the future of healthcare and its effects on patient care, exploring the emergence of electronic tools that will transform the medical industry. Explaining how technology, not politics, will lead the future of the healthcare revolution, author and healthcare technology expert Phil Fasano presents real-life examples that show how the next generation of medical breakthroughs will come from the instant exchange of information across the world Explores how new technologies will radically change the future of healthcare by making it easier to share information rapidly Explains what the future of the high tech medical industry means for investors and entrepreneurs Written by a respected healthcare and health technology expert Offering an unprecedented look at how technology is transforming the healthcare industry, and what it will mean for future investors and entrepreneurs, Transforming Health Care is a remarkable insight into the next generation of health technologies.

This is the second edition of Wil van der Aalst’s seminal book on process mining, which now discusses the field also in the broader context of data science and big data approaches. It includes several additions and updates, e.g. on inductive mining techniques, the notion of alignments, a considerably expanded section on software tools and a completely new chapter of process mining in the large. It is self-contained, while at the same time covering the entire process-mining spectrum from process discovery to predictive analytics. After a general introduction to data science and process mining in Part I, Part II provides the basics of business process modeling and data mining necessary to understand the remainder of the book. Next, Part III focuses on process discovery as the most important process mining task, while Part IV moves beyond discovering the control flow of processes, highlighting conformance checking, and organizational and time perspectives. Part V offers a guide to successfully applying process mining in practice, including an introduction to the widely used open-source tool ProM and several commercial products. Lastly, Part VI takes a step back, reflecting on the material presented and the key open challenges.  Overall, this book provides a comprehensive overview of the state of the art in process mining. It is intended for business process analysts, business consultants, process managers, graduate students, and BPM researchers.

Learn methods of data analysis and their application to real-world data sets This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis. The authors apply a unified “white box” approach to data mining methods and models. This approach is designed to walk readers through the operations and nuances of the various methods, using small data sets, so readers can gain an insight into the inner workings of the method under review. Chapters provide readers with hands-on analysis problems, representing an opportunity for readers to apply their newly-acquired data mining expertise to solving real problems using large, real-world data sets. Data Mining and Predictive Analytics: Offers comprehensive coverage of association rules, clustering, neural networks, logistic regression, multivariate analysis, and R statistical programming language Features over 750 chapter exercises, allowing readers to assess their understanding of the new material Provides a detailed case study that brings together the lessons learned in the book Includes access to the companion website, www.dataminingconsultant, with exclusive password-protected instructor content Data Mining and Predictive Analytics will appeal to computer science and statistic students, as well as students in MBA programs, and chief executives.

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Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets. As a result, readers are provided with the needed guidance to model and interpret complicated data and become adept at building powerful models for prediction and classification. Highlighting both underlying concepts and practical computational skills, Data Mining and Business Analytics with R begins with coverage of standard linear regression and the importance of parsimony in statistical modeling. The book includes important topics such as penalty-based variable selection (LASSO); logistic regression; regression and classification trees; clustering; principal components and partial least squares; and the analysis of text and network data. In addition, the book presents: · A thorough discussion and extensive demonstration of the theory behind the most useful data mining tools · Illustrations of how to use the outlined concepts in real-world situations · Readily available additional data sets and related R code allowing readers to apply their own analyses to the discussed materials · Numerous exercises to help readers with computing skills and deepen their understanding of the material Data Mining and Business Analytics with R is an excellent graduate-level textbook for courses on data mining and business analytics. The book is also a valuable reference for practitioners who collect and analyze data in the fields of finance, operations management, marketing, and the information sciences.

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The versatile capabilities and large set of add-on packages make R an excellent alternative to many existing and often expensive data mining tools. Exploring this area from the perspective of a practitioner, Data Mining with R: Learning with Case Studies uses practical examples to illustrate the power of R and data mining. Assuming no prior knowledge of R or data mining/statistical techniques, the book covers a diverse set of problems that pose different challenges in terms of size, type of data, goals of analysis, and analytical tools. To present the main data mining processes and techniques, the author takes a hands-on approach that utilizes a series of detailed, real-world case studies: Predicting algae blooms Predicting stock market returns Detecting fraudulent transactions Classifying microarray samples With these case studies, the author supplies all necessary steps, code, and data. Web ResourceA supporting website mirrors the do-it-yourself approach of the text. It offers a collection of freely available R source files that encompass all the code used in the case studies. The site also provides the data sets from the case studies as well as an R package of several functions.

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Spectral Feature Selection for Data Mining introduces a novel feature selection technique that establishes a general platform for studying existing feature selection algorithms and developing new algorithms for emerging problems in real-world applications. This technique represents a unified framework for supervised, unsupervised, and semisupervised feature selection. The book explores the latest research achievements, sheds light on new research directions, and stimulates readers to make the next creative breakthroughs. It presents the intrinsic ideas behind spectral feature selection, its theoretical foundations, its connections to other algorithms, and its use in handling both large-scale data sets and small sample problems. The authors also cover feature selection and feature extraction, including basic concepts, popular existing algorithms, and applications. A timely introduction to spectral feature selection, this book illustrates the potential of this powerful dimensionality reduction technique in high-dimensional data processing. Readers learn how to use spectral feature selection to solve challenging problems in real-life applications and discover how general feature selection and extraction are connected to spectral feature selection.

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We live in a world of big data: the amount of information collected on human behavior each day is staggering, and exponentially greater than at any time in the past. Additionally, powerful algorithms are capable of churning through seas of data to uncover patterns. Providing a simple and accessible introduction to data mining, Paul Attewell and David B. Monaghan discuss how data mining substantially differs from conventional statistical modeling familiar to most social scientists. The authors also empower social scientists to tap into these new resources and incorporate data mining methodologies in their analytical toolkits. Data Mining for the Social Sciences demystifies the process by describing the diverse set of techniques available, discussing the strengths and weaknesses of various approaches, and giving practical demonstrations of how to carry out analyses using tools in various statistical software packages.