Principles of Data Integration is the first comprehensive textbook of data integration, covering theoretical principles and implementation issues as well as current challenges raised by the semantic web and cloud computing. The book offers a range of data integration solutions enabling you to focus on what is most relevant to the problem at hand. Readers will also learn how to build their own algorithms and implement their own data integration application.
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.
This text is an ideal resource for database practitioners in industry, including data warehouse engineers, database system designers, data architects/enterprise architects, database researchers, statisticians, and data analysts; students in data analytics and knowledge discovery; and other data professionals working at the R&D and implementation levels.
- Offers a range of data integration solutions enabling you to focus on what is most relevant to the problem at hand
- Enables you to build your own algorithms and implement your own data integration applications
|Edition description:||New Edition|
|Product dimensions:||7.70(w) x 9.30(h) x 1.50(d)|
About the Author
AnHai Doan, Associate Professor in Computer Science at the University of Wisconsin-Madison. Consulting work with Microsoft AdCenter Lab and Yahoo Research Lab.Head of the Structured Data Group, Google Research, Mountain View, California. He joined Google in 2005 with the acquisition of his company, Transformic.Associate Professor at the University of Pennsylvania and a Faculty Member of the Penn Center for Bioinformatics. He received his PhD from the University of Washington. His research interests include data integration, data sharing among autonomous and heterogeneous systems, heterogeneous sensor networks, and information provenance and authoritativeness.
Table of Contents
CH 1: Introduction
Part I: Foundational Data Integration Techniques
CH 2: Manipulating Query Expressions
CH 3: Describing Data Sources
CH 4: String Matching CH 5: Schema Matching and Mapping
CH 6: General Schema Manipulation Operators
CH 7: Data Matching
CH 8: Query Processing
CH 9: Wrappers
CH 10: Data Warehousing and Caching
Part II: Integration with Extended Data Representations
CH 11: XML
CH 12: Ontologies and Knowledge Representation
CH 13: Incorporating Uncertainty into Data Integration
CH 14: Data Provenance
Part III: Novel Integration Architectures
CH 15: Data Integration on the Web
CH 16: Keyword Search: Integration on Demand
CH 17: Peer-to-Peer Integration
CH 18: Integration in Support of Collaboration
CH 19: The Future of Data Integration