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DOE OSTI · 1056553

Massive-scale RDF Processing Using Compressed Bitmap Indexes

Madduri, Kamesh·Wu, Kesheng
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Abstract

The Resource Description Framework (RDF) is a popular data model for representing linked data sets arising from the web, as well as large scienti c data repositories such as UniProt. RDF data intrinsically represents a labeled and directed multi-graph. SPARQL is a query language for RDF that expresses subgraph pattern- nding queries on this implicit multigraph in a SQL- like syntax. SPARQL queries generate complex intermediate join queries; to compute these joins e ciently, we propose a new strategy based on bitmap indexes. We store the RDF data in column-oriented structures as compressed bitmaps along with two dictionaries. This paper makes three new contributions. (i) We present an e cient parallel strategy for parsing the raw RDF data, building dictionaries of unique entities, and creating compressed bitmap indexes of the data. (ii) We utilize the constructed bitmap indexes to e ciently answer SPARQL queries, simplifying the join evaluations. (iii) To quantify the performance impact of using bitmap indexes, we compare our approach to the state-of-the-art triple-store RDF-3X. We nd that our bitmap index-based approach to answering queries is up to an order of magnitude faster for a variety of SPARQL queries, on gigascale RDF data sets.

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97 MATHEMATICS AND COMPUTINGcompressed bitmap indexeslarge-scale data analysisRDFsemantic dataSPARQL query optimization

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BibTeXRIS

Madduri, Kamesh, Wu, Kesheng. 2011-05-26. Massive-scale RDF Processing Using Compressed Bitmap Indexes. https://www.osti.gov/biblio/1056553

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