Abstract:
In this paper, we proposed a scalable RDF triple store for massive-scale RDF data that processes the SPARQL query with many join operations in efficient manner. Graph cha...Show MoreMetadata
Abstract:
In this paper, we proposed a scalable RDF triple store for massive-scale RDF data that processes the SPARQL query with many join operations in efficient manner. Graph characteristic of RDF data model hinders scalable and efficient indexing and querying over RDF triples. To address the problem, our query processing uses the pruning algorithm based on Bit-structure and summarized information to minimize data-reading. Our approach guarantees scalability and flexibility even for massive-scale RDF data by storing RDF triples in distributed fashion, providing the modifiable structure, and optimizing memory footprint of usage. The experiments shows that our system is better performing for queries with many join operations while uses less memory footprints.
Published in: 2015 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM)
Date of Conference: 24-26 August 2015
Date Added to IEEE Xplore: 30 November 2015
ISBN Information:
Electronic ISSN: 2154-5952