Relevant Query Answering over Streaming and Distributed Data

Name: Relevant Query Answering over Streaming and Distributed Data | A Study for RDF Streams and Evolving Web Data
Brand: Springer
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A Study for RDF Streams and Evolving Web Data

Shima Zahmatkesh Emanuele Della Valle(Autor*in)

Springer (Verlag)

1. Auflage

Erschienen am 21. Januar 2020

IX, 123 Seiten

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978-3-030-38339-8 (ISBN)

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Inhalt

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

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