Massive-Scale RDF Processing Using Compressed Bitmap Indexes

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 SQLlike syntax. SPARQL queries generate complex intermediate join queries; to compute these joins efficiently, 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 efficient 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 efficiently 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 find 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. Keywordssemantic data, RDF, SPARQL query optimization, compressed bitmap indexes, largescale data analysis