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.
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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 MatchingCH 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
