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Principles of Data Integration

Principles of Data Integration
  • Author : AnHai Doan,Alon Halevy,Zachary Ives
  • Publisher : Elsevier
  • Release : 25 June 2012
GET THIS BOOKPrinciples of Data Integration

How do you approach answering queries when your data is stored in multiple databases that were designed independently by different people? This is first comprehensive book on data integration and is written by three of the most respected experts in the field. This book provides an extensive introduction to the theory and concepts underlying today's data integration techniques, with detailed, instruction for their application using concrete examples throughout to explain the concepts. Data integration is the problem of answering queries


Principles of Data Integration

Principles of Data Integration
  • Author : AnHai Doan,Alon Halevy,Zachary G. Ives
  • Publisher : Elsevier
  • Release : 08 March 2021
GET THIS BOOKPrinciples of Data Integration

How do you approach answering queries when your data is stored in multiple databases that were designed independently by different people? This is first comprehensive book on data integration and is written by three of the most respected experts in the field. This book provides an extensive introduction to the theory and concepts underlying today's data integration techniques, with detailed, instruction for their application using concrete examples throughout to explain the concepts. Data integration is the problem of answering queries


Principles of Distributed Database Systems

Principles of Distributed Database Systems
  • Author : M. Tamer Özsu,Patrick Valduriez
  • Publisher : Springer Science & Business Media
  • Release : 24 February 2011
GET THIS BOOKPrinciples of Distributed Database Systems

This third edition of a classic textbook can be used to teach at the senior undergraduate and graduate levels. The material concentrates on fundamental theories as well as techniques and algorithms. The advent of the Internet and the World Wide Web, and, more recently, the emergence of cloud computing and streaming data applications, has forced a renewal of interest in distributed and parallel data management, while, at the same time, requiring a rethinking of some of the traditional techniques. This


Managing Data in Motion

Managing Data in Motion
  • Author : April Reeve
  • Publisher : Newnes
  • Release : 26 February 2013
GET THIS BOOKManaging Data in Motion

Managing Data in Motion describes techniques that have been developed for significantly reducing the complexity of managing system interfaces and enabling scalable architectures. Author April Reeve brings over two decades of experience to present a vendor-neutral approach to moving data between computing environments and systems. Readers will learn the techniques, technologies, and best practices for managing the passage of data between computer systems and integrating disparate data together in an enterprise environment. The average enterprise's computing environment is comprised of



Data Integration Blueprint and Modeling

Data Integration Blueprint and Modeling
  • Author : Anthony David Giordano
  • Publisher : Pearson Education
  • Release : 27 December 2010
GET THIS BOOKData Integration Blueprint and Modeling

Making Data Integration Work: How to Systematically Reduce Cost, Improve Quality, and Enhance Effectiveness Today’s enterprises are investing massive resources in data integration. Many possess thousands of point-to-point data integration applications that are costly, undocumented, and difficult to maintain. Data integration now accounts for a major part of the expense and risk of typical data warehousing and business intelligence projects--and, as businesses increasingly rely on analytics, the need for a blueprint for data integration is increasing now more than


Principles of Big Data

Principles of Big Data
  • Author : Jules J. Berman
  • Publisher : Newnes
  • Release : 20 May 2013
GET THIS BOOKPrinciples of Big Data

Principles of Big Data helps readers avoid the common mistakes that endanger all Big Data projects. By stressing simple, fundamental concepts, this book teaches readers how to organize large volumes of complex data, and how to achieve data permanence when the content of the data is constantly changing. General methods for data verification and validation, as specifically applied to Big Data resources, are stressed throughout the book. The book demonstrates how adept analysts can find relationships among data objects held


Data Architecture: A Primer for the Data Scientist

Data Architecture: A Primer for the Data Scientist
  • Author : W.H. Inmon,Daniel Linstedt,Mary Levins
  • Publisher : Academic Press
  • Release : 30 April 2019
GET THIS BOOKData Architecture: A Primer for the Data Scientist

Over the past 5 years, the concept of big data has matured, data science has grown exponentially, and data architecture has become a standard part of organizational decision-making. Throughout all this change, the basic principles that shape the architecture of data have remained the same. There remains a need for people to take a look at the "bigger picture" and to understand where their data fit into the grand scheme of things. Data Architecture: A Primer for the Data Scientist, Second


Attribution Principles for Data Integration

Attribution Principles for Data Integration
  • Author : Thomas Yupoo Lee,Massachusetts Institute of Technology. Technology, Management, and Policy Program
  • Publisher : Anonim
  • Release : 08 March 2021
GET THIS BOOKAttribution Principles for Data Integration

(cont.) The policy perspective encompasses not only what and where but also integration architectures and the relationships between data providers and users. Information technologies separate the processes and products of data gathering from data selection and presentation. Where the latter is addressed by copyright, the former is not addressed at all. Based upon two traditional, legal-economic frameworks, the asymmetric Prisoner's Dilemma and Entitlement Theory, we argue for a policy of misappropriation to support integration and attribution for data.


Data and Information Quality

Data and Information Quality
  • Author : Carlo Batini,Monica Scannapieco
  • Publisher : Springer
  • Release : 23 March 2016
GET THIS BOOKData and Information Quality

This book provides a systematic and comparative description of the vast number of research issues related to the quality of data and information. It does so by delivering a sound, integrated and comprehensive overview of the state of the art and future development of data and information quality in databases and information systems. To this end, it presents an extensive description of the techniques that constitute the core of data and information quality research, including record linkage (also called object


Linked Data Management

Linked Data Management
  • Author : Andreas Harth,Katja Hose,Ralf Schenkel
  • Publisher : CRC Press
  • Release : 19 April 2016
GET THIS BOOKLinked Data Management

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



Developing High Quality Data Models

Developing High Quality Data Models
  • Author : Matthew West
  • Publisher : Elsevier
  • Release : 07 February 2011
GET THIS BOOKDeveloping High Quality Data Models

Developing High Quality Data Models provides an introduction to the key principles of data modeling. It explains the purpose of data models in both developing an Enterprise Architecture and in supporting Information Quality; common problems in data model development; and how to develop high quality data models, in particular conceptual, integration, and enterprise data models. The book is organized into four parts. Part 1 provides an overview of data models and data modeling including the basics of data model notation; types


Building a Data Integration Team

Building a Data Integration Team
  • Author : Jarrett Goldfedder
  • Publisher : Apress
  • Release : 27 February 2020
GET THIS BOOKBuilding a Data Integration Team

Find the right people with the right skills. This book clarifies best practices for creating high-functioning data integration teams, enabling you to understand the skills and requirements, documents, and solutions for planning, designing, and monitoring both one-time migration and daily integration systems. The growth of data is exploding. With multiple sources of information constantly arriving across enterprise systems, combining these systems into a single, cohesive, and documentable unit has become more important than ever. But the approach toward integration is


Data Lakes

Data Lakes
  • Author : Anne Laurent,Dominique Laurent,Cédrine Madera
  • Publisher : John Wiley & Sons
  • Release : 09 April 2020
GET THIS BOOKData Lakes

The concept of a data lake is less than 10 years old, but they are already hugely implemented within large companies. Their goal is to efficiently deal with ever-growing volumes of heterogeneous data, while also facing various sophisticated user needs. However, defining and building a data lake is still a challenge, as no consensus has been reached so far. Data Lakes presents recent outcomes and trends in the field of data repositories. The main topics discussed are the data-driven architecture of