Quality-Driven Query Answering for Integrated Information Systems (Lecture Notes in Computer Science, 2261)

Lecture Notes in Computer Science
Springer
Quality-Driven Query Answering for Integrated Information Systems
Foreword
Preface
	Acknowledgments
Contents
	Part I. Querying the Web
		Introduction
		Integrating Autonomous Information Sources
	Part II. Information Quality
		Information Quality Criteria
		Quality Ranking Methods
	Part III. Quality-Driven Query Answering
		Quality-Driven Query Planning
		Query Planning Revisited
		Completeness of Data
		Completeness-Driven Query Optimization
	Part IV. Discussion
		Conclusion
	References
1 Introduction
	1.1 Centralized Databases Vs. the World Wide Web
	1.2 Information Quality on the Web
	1.3 Problem Definition
	1.4 Thesis Outline
2 Integrating Autonomous Information Sources
	2.1 The Mediator-Wrapper Architecture
	2.2 The Universal Relation
	2.3 Information Overlap
	2.4 Applications
	2.5 Related Work
	2.6 Summary
3 Information Quality Criteria
	3.1 Information Quality Criteria for the Web
		3.1.1 IQ-criteria
		3.1.2 Criterion Selection for Web Information Systems
		3.1.3 Related Work
	3.2 Information Quality Assessment
		3.2.1 An Assessment-Oriented Classification
		3.2.2 Assessment Methods
		3.2.3 Metadata Models for IQ-assessment
		3.2.4 Related Work
	3.3 Summary
4 Quality Ranking Methods
	4.1 Quality Model
	4.2 Scaling Methods
	4.3 User Weighting
	4.4 Ranking Methods
		4.4.1 Simple Additive Weighting (Saw)
		4.4.2 Technique for Order Preference by Similarity to Ideal Solution
		4.4.3 Analytical Hierarchy Process (AHP)
		4.4.4 Elimination et Choice Translating Reality (ELECTRE)
		4.4.5 Data Envelopment Analysis (DEA)
	4.5 Comparison and Evaluation
		4.5.1 Properties of the Ranking Methods
		4.5.2 Evaluation of the Ranking Methods
	4.6 Summary
5 Quality-Driven Query Planning
	5.1 Logical Query Planning
		5.1.1 Query Translation
		5.1.2 Query Planning
		5.1.3 Top N Plans
		5.1.4 Merge Functions for IQ-criteria
		5.1.5 Relationship to Conventional Query Optimization
	5.2 Attaching Quality Reasoning to Query Planning
		5.2.1 Phase 1: Source Selection
		5.2.2 Phase 2: Plan Creation
		5.2.3 Phase 3: Plan Selection
		5.2.4 Computational Complexity
	5.3 Integrating Quality Reasoning and Query Planning
		5.3.1 The HiQ B&B Algorithm
		5.3.2 Information Quality Bounds
		5.3.3 Evaluation
	5.4 Related Work
		5.4.1 Top N Results
		5.4.2 Query Planning
	5.5 Summary
6 Query Planning Revisited
	6.1 Shortcomings of Conventional Query Planning
	6.2 Merge Operators
		6.2.2 The Left Outerjoin-Merge Operator
		6.2.3 The Full Outerjoin-Merge Operator
	6.3 Revised Logical Query Planning
		6.3.1 Query Translation
		6.3.2 Query Planning
	6.4 Related Work
	6.5 Summary
7 Completeness of Data
	7.1 A Completeness Measure for Sources
		7.1.1 Coverage
		7.1.2 Density
		7.1.3 Completeness
		7.1.4 Extensions
	7.2 A Completeness Measure for Plans
		7.2.1 Overlap Situations
		7.2.2 Merging Coverage
		7.2.3 Merging Density
		7.2.4 Merging Completeness
	7.3 Properties of the Measures
	7.4 Other Overlap Situations
	7.5 Related Work
		7.5.1 Completeness of Single Sources
		7.5.2 Completeness of Merged Sources
	7.6 Summary
8 Completeness-Driven Query Optimization
	8.1 Completeness Maximization
		8.1.1 Cost Model
		8.1.2 Problem Definition
		8.1.3 Complexity Analysis
	8.2 Maximizing Coverage
		8.2.1 Algorithms for Simple Overlap Profiles
		8.2.2 Algorithms for Complex Overlap Profiles
		8.2.3 Performance Evaluation
		8.2.4 Adding Availability Constraints
	8.3 Maximizing Completeness
		8.3.1 The Greedy Look-Ahead Algorithm
		8.3.2 Performance Evaluation
	8.4 Algebraic Reordering
	8.5 Summary
9 Conclusion
	9.1 Summary
	9.2 Further Applications for IQ-reasoning
		9.2.1 Data Warehouses
		9.2.2 Data Mining
		9.2.3 Quality-Driven Integration of Query Results
	9.3 An Appeal
References