Relevant Query Answering over Streaming and Distributed Data
Description
This book examines the problem of relevant query answering over the Web and provides a comprehensive overview of relevant query answering over streaming and distributed data. In recent years, Web applications that combine highly dynamic data streams with data distributed over the Web to provide relevant answers have attracted increasing attention. Answering in a timely fashion, i.e., reactively, is one of the most important performance indicators, especially when the distributed data is evolving. The book proposes a solution that retains a local replica of the distributed data and offers various maintenance policies to refresh the replica over time. A limited refresh budget guarantees the reactiveness of the system. Focusing on stream processing and Semantic Web, it appeals to scientists and graduate students in the field.
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Content
- Intro
- Preface
- Contents
- 1 Introduction
- 1.1 Motivation
- 1.2 Problem Statement and Research Question
- 1.3 Approach and Contributions
- 1.4 Structure of the Book
- References
- 2 Background
- 2.1 RDF Data Model and SPARQL Query Language
- 2.2 Federated SPARQL Engines
- 2.3 RSP-QL Semantic
- 2.4 Top-k Query Answering
- 2.5 Top-k Query Monitoring Over the Data Stream
- 2.6 Metrics
- References
- 3 ACQUA: Approximate Continuous Query Answering in RSP
- 3.1 Introduction
- 3.2 Problem Statement
- 3.3 Proposed Solution
- 3.3.1 Window Service Join Method
- 3.3.2 Window Based Maintenance Policy
- 3.4 Experiments
- 3.4.1 Experimental Setting
- 3.4.2 Experiment 1-Comparison of Proposers
- 3.4.3 Experiment 2-Comparison of Maintenance Polices
- 3.5 Related Work
- 3.6 Conclusion
- References
- 4 Handling Queries with a FILTER Clause
- 4.1 Introduction
- 4.2 Problem Statement
- 4.3 Proposed Solution
- 4.3.1 Filter Update Policy
- 4.3.2 ACQUA.F Policies
- 4.4 Experiments
- 4.4.1 Hypotheses
- 4.4.2 Experimental Setting
- 4.4.3 Experiment 1-Sensitivity for Filter Update Policy
- 4.4.4 Experiment 2-Sensitivity for ACQUA.F Policies
- 4.5 Outlook
- References
- 5 Rank Aggregation in Queries with a FILTER Clause
- 5.1 Introduction
- 5.2 Rank Aggregation
- 5.3 Problem Statement
- 5.4 Rank Aggregation Solution
- 5.4.1 ACQUA.F+ Policy
- 5.4.2 WBM.F* Policy
- 5.5 Experiments
- 5.5.1 Research Hypotheses
- 5.5.2 Experimental Setting
- 5.5.3 Relaxing ACQUA.F Assumption
- 5.5.4 Experiment 1-Sensitivity to the Filter Selectivity
- 5.5.5 Experiment 2-Sensitivity to the Refresh Budget
- 5.6 Outlook
- References
- 6 Handling Top-k Queries
- 6.1 Introduction
- 6.2 Problem Statement
- 6.3 Topk+N Solution
- 6.3.1 MinTopk+
- 6.3.2 Updating Minimal Top-K+N Candidate List
- 6.3.3 Super-MTK+N List
- 6.3.4 Topk+N Algorithm
- 6.4 AcquaTop Solution
- 6.4.1 AcquaTop Framework
- 6.4.2 AcquaTop Algorithm
- 6.4.3 Cost Analysis
- 6.5 Evaluation
- 6.5.1 Experimental Setting
- 6.5.2 Preliminary Experiment
- 6.5.3 Research Hypotheses
- 6.5.4 Experiment 1-Sensitivity to the Refresh Budget
- 6.5.5 Experiment 2-Sensitivity to Change Frequency (CH)
- 6.5.6 Experiment 3-Sensitivity to K
- 6.5.7 Experiment 4-Sensitivity to N
- 6.5.8 Wrap up
- 6.6 Related Work
- 6.7 Outlook
- References
- 7 Conclusion
- 7.1 Suggestions for Future Works
- References
- Index
