Relevant Query Answering over Streaming and Distributed Data: A Study for RDF Streams and Evolving Web Data (PoliMI SpringerBriefs)

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