Typed data graph summarization

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Table of contents

1 Introduction 
1.1 Organizational aspects
1.2 Scientific outline
1.3 Report structure
2 Preliminaries 
2.1 Data graphs
2.2 Summarization framework
2.3 Data graph summarization
2.3.1 Data property cliques
2.3.2 Strong and weak node equivalences
2.3.3 Weak and strong summarization
2.4 Typed data graph summarization
2.4.1 Data-then-type summarization
2.4.2 Type-then-data summarization
2.5 RDF graph summarization
2.5.1 Extending summarization
2.5.2 Summarization versus saturation
3 Summarization aware of type hierarchies 
3.1 Novel type-based RDF equivalence
3.2 RDF summary based on type hierarchy equivalence
4 Graph summarization algorithms 
4.1 Centralized summarization algorithms
4.1.1 Data graph summarization
4.1.2 Typed graph summarization
4.2 Type-hierarchy-based summarization algorithms
4.2.1 Constructing the weak type-hierarchy summary
4.2.2 Applicability
4.3 Distributed algorithms
4.3.1 Parallel computation of the strong summary
4.3.2 Parallel computation of the weak summary
4.3.3 Parallel computation of the typed strong and typed weak summaries
4.3.4 Apache Spark implementation specifics
5 Experiments 
5.1 Centralized algorithms experiments
5.2 Distributed algorithms experiments
5.2.1 Cluster setup
5.2.2 Configuration
5.2.3 Speed up thanks to the increase of the degree of parallelism
6 RelatedWork 
7 Conclusion