Principles of Data Integration

Principles of Data Integration, 1st Edition

Principles of Data Integration, 1st Edition,AnHai Doan,Alon Halevy,Zachary Ives,ISBN9780124160446

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Morgan Kaufmann




265 X 190

The first comprehensive textbook of data integration from theoretical principles to implementation issues and current challenges raised by the semantic web and cloud computing

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Key Features

  • 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
  • Companion website with numerous project-based exercises and solutions and slides. Links to commercially available software allowing readers to build their own algorithms and implement their own data integration applications. Facebook page for reader input during and after publication


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 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.


Database 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

AnHai Doan

AnHai Doan, Associate Professor in Computer Science at the University of Wisconsin-Madison. Consulting work with Microsoft AdCenter Lab and Yahoo Research Lab.

Affiliations and Expertise

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. He joined Google in 2005 with the acquisition of his company, Transformic.

Affiliations and Expertise

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. 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.

Affiliations and Expertise

Associate Professor at the University of Pennsylvania, and a Faculty Member of the Penn Center for Bioinformatics.

Principles of Data Integration, 1st Edition



1. Introduction

1.1 What Is Data Integration?

1.2 Why Is It Hard?

1.3 Data Integration Architectures

1.4 Outline of the Book

Bibliographic Notes

Part I: Foundational Data Integration Techniques

2. Manipulating Query Expressions

2.1 Review of Database Concepts

2.2 Query Unfolding

2.3 Query Containment and Equivalence

2.4 Answering Queries Using Views

Bibliographic Notes

3. Describing Data Sources

3.1 Overview and Desiderata

3.2 Schema Mapping Languages

3.3 Access-Pattern Limitations

3.4 Integrity Constraints on the Mediated Schema

3.5 Answer Completeness

3.6 Data-Level Heterogeneity

Bibliographic Notes

4. String Matching

4.1 Problem Description

4.2 Similarity Measures

4.3 Scaling Up String Matching

Bibliographic Notes

5. Schema Matching and Mapping

5.1 Problem Definition

5.2 Challenges of Schema Matching and Mapping

5.3 Overview of Matching and Mapping Systems

5.4 Matchers

5.5 Combining Match Predictions

5.6 Enforcing Domain Integrity Constraints

5.7 Match Selector

5.8 Reusing Previous Matches

5.9 Many-to-Many Matches

5.10 From Matches to Mappings

Bibliographic Notes

6. General Schema Manipulation Operators

6.1 Model Management Operators

6.2 Merge

6.3 ModelGen

6.4 Invert

6.5 Toward Model Management Systems

6.5 Bibliographic Notes

7. Data Matching

7.1 Problem Definition

7.2 Rule-Based Matching

7.3 Learning-Based Matching

7.4 Matching by Clustering

7.5 Probabilistic Approaches to Data Matching

7.6 Collective Matching

7.7 Scaling Up Data Matching

Bibliographic Notes

8. Query Processing

8.1 Background: DBMS Query Processing

8.2 Background: Distributed Query Processing

8.3 Query Processing for Data Integration

8.4 Generating Initial Query Plans

8.5 Query Execution for Internet Data

8.6 Overview of Adaptive Query Processing

8.7 Event-Driven Adaptivity

8.8 Performance-Driven Adaptivity

Bibliographic Notes

9. Wrappers

9.1 Introduction

9.2 Manual Wrapper Construction

9.3 Learning-Based Wrapper Construction

9.4 Wrapper Learning without Schema

9.5 Interactive Wrapper Construction

Bibliographic Notes

10. Data Warehousing and Caching

10.1 Data Warehousing

10.2 Data Exchange: Declarative Warehousing

10.3 Caching and Partial Materialization

10.4 Direct Analysis of Local, External Data

Bibliographic Notes

Part II: Integration with Extended Data Representations

11. XML

11.1 Data Model

11.2 XML Structural and Schema Definitions

11.3 Query Language

11.4 Query Processing for XML

11.5 Schema Mapping for XML

Bibliographic Notes

12. Ontologies and Knowledge Representation

12.1 Example: Using KR in Data Integration

12.2 Description Logics

12.3 The Semantic Web

Bibliographic Notes

13. Incorporating Uncertainty into Data Integration

13.1 Representing Uncertainty

13.2 Modeling Uncertain Schema Mappings

13.3 Uncertainty and Data Provenance

Bibliographic Notes

14. Data Provenance

14.1 The Two Views of Provenance

14.2 Applications of Data Provenance

14.3 Provenance Semirings

14.4 Storing Provenance

Bibliographic Notes

Part III: Novel Integration Architectures

15. Data Integration on the Web

15.1 What Can We Do with Web Data?

15.2 The Deep Web

15.3 Topical Portals

15.4 Lightweight Combination of Web Data

15.5 Pay-as-You-Go Data Management

Bibliographic Notes

16. Keyword Search

16.1 Keyword Search over Structured Data

16.2 Computing Ranked Results

16.3 Keyword Search for Data Integration

Bibliographic Notes

17. Peer-to-Peer Integration

17.1 Peers and Mappings

17.2 Semantics of Mappings

17.3 Complexity of Query Answering in PDMS

17.4 Query Reformulation Algorithm

17.5 Composing Mappings

17.6 Peer Data Management with Looser Mappings

Bibliographic Notes

18. Integration in Support of Collaboration

18.1 What Makes Collaboration Different

18.2 Processing Corrections and Feedback

18.3 Collaborative Annotation and Presentation

18.4 Dynamic Data: Collaborative Data Sharing

Bibliographic Notes

19. The Future of Data Integration

19.1 Uncertainty, Provenance, and Cleaning

19.2 Crowdsourcing and “Human Computing”

19.3 Building Large-Scale Structured Web Databases

19.4 Lightweight Integration

19.5 Visualizing Integrated Data

19.6 Integrating Social Media

19.7 Cluster- and Cloud-Based Parallel Processing and Caching



Quotes and reviews

"Researchers looking for concise and clear descriptions of the state of the art in data integration will benefit from this noteworthy effort. Graduate students in particular will acquire an excellent blueprint of the field, supplemented by almost 600 up-to-date bibliographic references they can use to further their work." --ComputingReviews.com, October 2013

"Written by three of the field’s leading experts, this book manages to address a broad range of topics in its subject domain in a reasonably compact package…The intended audience is primarily academic, specifically graduate and advanced undergraduate students in a university setting. Researchers new to the field will find it to be a helpful introduction." --ComputingReviews.com, August 2013

"…a well-organized and thorough treatment of data integration topics is a welcome addition to the practicing software professional’s bookshelf. If that treatment is both academically rigorous and still readable, as is the case with this book, it becomes a valuable resource for researchers and, in particular, for doctoral students." --ComputingReviews.com, July 2013

"This is the definitive book on data integration technology, written by experts who invented much of the technology they write about. It’s comprehensive, with lots of technical detail very clearly explained. It’s a must-read for anyone involved in the development of data integration solutions." --Philip A. Bernstein, Distinguished Scientist, Microsoft Corporation

"Despite having been with us for decades, data integration remains a challenging, multi-faceted problem.  This book does an excellent job of bringing together and explaining its many facets along with the technical solutions that have been developed to date. The authors are three of the field's leading contributors, with a mix of both academic and industrial experience, and their presentation includes examples and manages to make even the more theoretical material accessible to readers.  All aspects of modern data integration are covered, including different styles of integration, data and schema matching, query processing and wrappers, as well as challenges posed by the Web and the wide variety of data types and formats that must be integrated today.  This book should be a great resource for graduate courses on data integration." --Michael Carey, Bren Professor of Information and Computer Sciences, UC Irvine

"The days of enterprises/organizations depending on a single, closed database have given way to a Web-dominated world in which multiple databases must interoperate and integrate. Doan (computer science, U. of Wisconsin, Madison) and colleagues at Google and the University of Pennsylvania address how database ideas have broadened to accommodate external sources of structured information, distributed aspects of the Web, and issues of data-sharing. Part I treats topics and techniques for data queries, integration, and warehousing covered in a database course. Part II discusses extended data representations that capture properties not present in the standard relational data model. Then they present novel architectures for, and trends in, addressing specific integration problems, e.g., of Web sources. Includes an extensive bibliography." --Reference and Research Book News, October 2012

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