Principles of Data Integration

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.

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