Document-based RDF storage method for parallel evaluation of basic graph pattern queries. (16th June 2020)

• Record Type: Journal Article
• Authors: Kalogeros, Eleftherios; Gergatsoulis, Manolis; Damigos, Matthew
• Is Part Of: International journal of metadata, semantics and ontologies. Volume 14:Number 1(2020)
• Page Start: 63
• Page End: 80
• Publication Date: 2020-06-16
• Subjects: semantic web -- parallel processing -- query processing -- resource description framework -- big data applications; Metadata -- Periodicals; Semantic Web -- Periodicals; Ontologies (Information retrieval) -- Periodicals; Data structures (Computer science) -- Periodicals; Information theory -- Periodicals; 005.74
• Languages: English
• ISSNs: 1744-2621

Abstract

In this paper, we investigate the problem of efficiently evaluating (Basic Graph Pattern) BGP SPARQL queries over a large amount of RDF data. We propose an effective data model for storing RDF data in a document database using maximum replication factor of 2 (i.e., in the worst case scenario, the data graph will be doubled in storage size). The proposed storage model is utilised for efficiently evaluating SPARQL queries, in a distributed manner. Each query is decomposed into a set of generalised star queries, which are queries that allow both subject-object and object-subject edges from a specific node, called central node. The proposed data model ensures that no joining operations over multiple data sets are required to evaluate generalised star queries. The results of the evaluation of the generalised star sub-queries of a query Q are then combined properly, in order to compute the answers of the query Q posed over the RDF data. The proposed approach has been implemented using MongoDB and Apache Spark.