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Data Partitioning Scheme for Efficient Distributed RDF Querying Using Apache Spark | IEEE Conference Publication | IEEE Xplore

Data Partitioning Scheme for Efficient Distributed RDF Querying Using Apache Spark


Abstract:

The rapid growth of semantic data in the form of Resource Description Framework (RDF) triples demands an efficient, scalable, and distributed storage and parallel process...Show More

Abstract:

The rapid growth of semantic data in the form of Resource Description Framework (RDF) triples demands an efficient, scalable, and distributed storage and parallel processing strategies along with high availability and fault tolerance for its management and reuse. There are three open issues with distributed RDF data management systems that are not well addressed altogether in existing work. First is the querying efficiency, second, solutions are optimized for certain types of query patterns and don't necessarily work well for all types of query patterns, and the third is concerned with reducing pre-processing and data loading times. To address these issues, we propose a relational partitioning scheme called Subset Property Table (SPT) for RDF data that further partitions the existing Property Table approach into subsets of tables to minimize query input and join operation. We combine SPT with another existing model Vertical Partitioning (VP) for storing RDF datasets and demonstrate that our proposed combined (SPT + VP) approach outperforms state-of-the-art systems based on in-memory processing engine in a distributed environment.
Date of Conference: 30 January 2019 - 01 February 2019
Date Added to IEEE Xplore: 14 March 2019
ISBN Information:
Print on Demand(PoD) ISSN: 2325-6516
Conference Location: Newport Beach, CA, USA

References

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