Cost Analysis of Joins in RDF Query Processing Using the TripleT Index Open Access

Li, Kanwei (2009)

Permanent URL: https://etd.library.emory.edu/concern/etds/tb09j615c?locale=en
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Abstract

The Semantic Web movement has led to a growing popularity of RDF and its query languages. Clearly, good query performance is important in allowing information to be quickly retrieved from RDF datasets that are ever-increasing in size. We use the TripleT indexing scheme for RDF data as a framework to examine the cost of join operations for RDF. We analyze strategies for efficient join processing for a variety of query patterns. For queries that involve multiple join conditions, we introduce a model to predict the number of I/Os required to best order the join conditions. Experimental results validate the model using three real RDF datasets.

Table of Contents

1 Introduction
1.1 Research Objective
1.2 Prior Work
2 RDF and SPARQL
2.1 Background on RDF
2.1.1 Representation Formats
2.2 RDF Datasets
2.3 Datasets Used in the Thesis
2.3.1 Dataset Statistics
2.3.2 Dataset Discussion
2.4 Background on SPARQL
3 Indexing Techniques
3.1 B+ Trees
3.2 RDF Indexing Schemes
3.2.1 MAP and HexTree
3.2.2 TripleT
4 Join Algorithms
4.1 Nested-loop join
4.2 Hash join
4.3 Sort-Merge join
4.4 Measuring Join Performance
4.4.1 Join CPU Performance on Synthetic Data
4.4.2 Join I/O and CPU Performance on Datasets
5 Query Optimization
5.1 Join Ordering
5.2 Processing SPARQL Queries with TripleT
5.3 Discussion
6 Models and Experiments for All-Variable SAPs
6.1 DBpedia Results
6.2 Uniprot Results
6.3 SP2Bench Results
6.4 Variant Query Forms
6.5 Discussion

7 Conclusion

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