Table of Contents
1.3 Data Integration Architectures
Part I: Foundational Data Integration Techniques
2. Manipulating Query Expressions
2.1 Review of Database Concepts
2.3 Query Containment and Equivalence
2.4 Answering Queries Using Views
3.3 Access-Pattern Limitations
3.4 Integrity Constraints on the Mediated Schema
4.3 Scaling Up String Matching
5. Schema Matching and Mapping
5.2 Challenges of Schema Matching and Mapping
5.3 Overview of Matching and Mapping Systems
5.5 Combining Match Predictions
5.6 Enforcing Domain Integrity Constraints
6. General Schema Manipulation Operators
6.1 Model Management Operators
6.5 Toward Model Management Systems
7.5 Probabilistic Approaches to Data Matching
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
8.8 Performance-Driven Adaptivity
9.2 Manual Wrapper Construction
9.3 Learning-Based Wrapper Construction
9.4 Wrapper Learning without Schema
9.5 Interactive Wrapper Construction
10. Data Warehousing and Caching
10.2 Data Exchange: Declarative Warehousing
10.3 Caching and Partial Materialization
10.4 Direct Analysis of Local, External Data
Part II: Integration with Extended Data Representations
11.2 XML Structural and Schema Definitions
12. Ontologies and Knowledge Representation
12.1 Example: Using KR in Data Integration
13. Incorporating Uncertainty into Data Integration
13.2 Modeling Uncertain Schema Mappings
13.3 Uncertainty and Data Provenance
14.1 The Two Views of Provenance
14.2 Applications of Data Provenance
Part III: Novel Integration Architectures
15. Data Integration on the Web
15.1 What Can We Do with Web Data?
15.4 Lightweight Combination of Web Data
15.5 Pay-as-You-Go Data Management
16.1 Keyword Search over Structured Data
16.3 Keyword Search for Data Integration
17.3 Complexity of Query Answering in PDMS
17.4 Query Reformulation Algorithm
17.6 Peer Data Management with Looser Mappings
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
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
19.5 Visualizing Integrated Data
19.7 Cluster- and Cloud-Based Parallel Processing and Caching