Massive-scale RDF processing using compressed bitmap indexes

Kamesh Madduri, Kesheng Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

The Resource Description Framework (RDF) is a popular data model for representing linked data sets arising from the web, as well as large scientific 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-finding queries on this implicit multigraph in a SQL-like syntax. SPARQL queries generate complex intermediate join queries; to compute these joins efficiently, this paper presents a new strategy based on bitmap indexes. We store the RDF data in column-oriented compressed bitmap structures, along with two dictionaries. We find that our bitmap index-based query evaluation approach is up to an order of magnitude faster the state-of-the-art system RDF-3X, for a variety of SPARQL queries on gigascale RDF data sets.

Original languageEnglish (US)
Title of host publicationScientific and Statistical Database Management - 23rd International Conference, SSDBM 2011, Proceedings
Pages470-479
Number of pages10
DOIs
StatePublished - 2011
Event23rd International Conference on Scientific and Statistical Database Management, SSDBM 2011 - Portland, OR, United States
Duration: Jul 20 2011Jul 22 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6809 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other23rd International Conference on Scientific and Statistical Database Management, SSDBM 2011
CountryUnited States
CityPortland, OR
Period7/20/117/22/11

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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