Principles of Data Integration
- AnHai Doan, Associate Professor in Computer Science at the University of Wisconsin-Madison. Consulting work with Microsoft AdCenter Lab and Yahoo Research Lab.
- Alon Halevy, Head of the Structured Data Group, Google Research, Mountain View, California.
- Zachary Ives, Associate Professor at the University of Pennsylvania, and a Faculty Member of the Penn Center for Bioinformatics.
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 that span multiple data sources (e.g., databases, web pages). Data integration problems surface in multiple contexts, including enterprise information integration, query processing on the Web, coordination between government agencies and collaboration between scientists. In some cases, data integration is the key bottleneck to making progress in a field.
The authors provide a working knowledge of data integration concepts and techniques, giving you the tools you need to develop a complete and concise package of algorithms and applications.
AudienceDatabase practitioners in industry, i.e., data warehouse engineers, database system designers, data architects/enterprise architects, database researchers, statisticians, data analysts, and other data professionals working at the R&D and implementation levels. Students in data analytics and knowledge discovery.
- Published: June 2012
- Imprint: MORGAN KAUFMANN
- ISBN: 978-0-12-416044-6
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