This second edition of Data Management Using Stata focuses on tasks that bridge the gap between raw data and statistical analysis. It has been updated throughout to reflect new data management features that have been added over the last 10 years. Such features include the ability to read and write a wide variety of file formats, the ability to write highly customized Excel files, the ability to have multiple Stata datasets open at once, and the ability to store and manipulate string variables stored as Unicode. Further, this new edition includes a new chapter illustrating how to write Stata programs for solving data management tasks. As in the original edition, the chapters are organized by data management areas: reading and writing datasets, cleaning data, labeling datasets, creating variables, combining datasets, processing observations across subgroups, changing the shape of datasets, and programming for data management. Within each chapter, each section is a self-contained lesson illustrating a particular data management task (for instance, creating date variables or automating error checking) via examples. This modular design allows you to quickly identify and implement the most common data management tasks without having to read background information first. In addition to the "nuts and bolts" examples, author Michael Mitchell alerts users to common pitfalls (and how to avoid them) and provides strategic data management advice. This book can be used as a quick reference for solving problems as they arise or can be read as a means for learning comprehensive data management skills. New users will appreciate this book as a valuable way to learn data management, while experienced users will find this information to be handy and time saving--there is a good chance that even the experienced user will learn some new tricks.

The Handbook provides practitioners, scientists and graduate students with a good overview of basic notions, methods and techniques, as well as important issues and trends across the broad spectrum of data management. In particular, the book covers fundamental topics in the field such as distributed databases, parallel databases, advanced databases, object-oriented databases, advanced transaction management, workflow management, data warehousing, data mining, mobile computing, data integration and the Web. Summing up, the Handbook is a valuable source of information for academics and practitioners who are interested in learning the key ideas in the considered area.

Written by leading industry experts, the Data Management Handbook is a comprehensive, single-volume guide to the most innovative ideas on how to plan, develop, and run a powerful data management function - as well as handle day-to-day operations. The book provides practical, hands-on guidance on the strategic, tactical, and technical aspects of dat

The overall objective of this book is to show that data management is an exciting and valuable capability that is worth time and effort. More specifically it aims to achieve the following goals: 1. To give a “gentle” introduction to the field of DM by explaining and illustrating its core concepts, based on a mix of theory, practical frameworks such as TOGAF, ArchiMate, and DMBOK, as well as results from real-world assignments. 2. To offer guidance on how to build an effective DM capability in an organization.This is illustrated by various use cases, linked to the previously mentioned theoretical exploration as well as the stories of practitioners in the field. The primary target groups are: busy professionals who “are actively involved with managing data”. The book is also aimed at (Bachelor’s/ Master’s) students with an interest in data management. The book is industry-agnostic and should be applicable in different industries such as government, finance, telecommunications etc. Typical roles for which this book is intended: data governance office/ council, data owners, data stewards, people involved with data governance (data governance board), enterprise architects, data architects, process managers, business analysts and IT analysts. The book is divided into three main parts: theory, practice, and closing remarks. Furthermore, the chapters are as short and to the point as possible and also make a clear distinction between the main text and the examples. If the reader is already familiar with the topic of a chapter, he/she can easily skip it and move on to the next.

Linked Data Management presents techniques for querying and managing Linked Data that is available on today’s Web. The book shows how the abundance of Linked Data can serve as fertile ground for research and commercial applications. The text focuses on aspects of managing large-scale collections of Linked Data. It offers a detailed introduction to Linked Data and related standards, including the main principles distinguishing Linked Data from standard database technology. Chapters also describe how to generate links between datasets and explain the overall architecture of data integration systems based on Linked Data. A large part of the text is devoted to query processing in different setups. After presenting methods to publish relational data as Linked Data and efficient centralized processing, the book explores lookup-based, distributed, and parallel solutions. It then addresses advanced topics, such as reasoning, and discusses work related to read-write Linked Data for system interoperation. Despite the publication of many papers since Tim Berners-Lee developed the Linked Data principles in 2006, the field lacks a comprehensive, unified overview of the state of the art. Suitable for both researchers and practitioners, this book provides a thorough, consolidated account of the new data publishing and data integration paradigm. While the book covers query processing extensively, the Linked Data abstraction furnishes more than a mechanism for collecting, integrating, and querying data from the open Web—the Linked Data technology stack also allows for controlled, sophisticated applications deployed in an enterprise environment.

Packed with dozens of no-nonsense chapters written by leading professionals, Handbook of Data Management, 1999 Edition shows your students how to design, build, and maintain high-performance, high-availability databases in multiple environments. Handbook of Data Management, 1999 Edition is the most comprehensive, single-volume guide of its kind. The book provides the latest, most innovative solutions for planning, developing, and running a powerful data management function. Here students will find exhaustive coverage of the range of data repositories (from legacy indexed files to object data bases and data warehouses) as well as details on everything from strategic planning to maximizing database performance. Completely revised and updated to reflect latebreaking technologies, Handbook of Data Management, 1999 Edition includes extensive case studies and straightforward descriptions showing students how to: implement Web-enabled data warehouses build multimedia databases master data mining use enterprise database modeling stay up-to-date with data conversion and migration maximize OLAP architectures and tools Handbook of Data Management, 1999 Edition also provides ongoing coverage of the latest tools and techniques regarding: organization for quality information systems data definition database design and management object and hybrid databases and more Each contributor to Handbook of Data Management, 1999 Edition is an expert with first-hand experience in database and data management. These contributors provide a depth and breadth of coverage you and your students simply won't find anywhere else. Prepare your students for "real-world" business computing. Start them off with Handbook of Data Management, 1999 Edition.