Paper Info
Title
Sparklify: A Scalable Software Component for Efficient Evaluation of SPARQL Queries over Distributed RDF Datasets.
Abstract
One of the key traits of Big Data is its complexity in terms of representation, structure, or formats. One existing way to deal with it is offered by Semantic Web standards. Among them, RDF - which proposes to model data with triples representing edges in a graph - has received a large success and the semantically annotated data has grown steadily towards a massive scale. Therefore, there is a need for scalable and efficient query engines capable of retrieving such information. In this paper, we propose Sparklify: a scalable software component for efficient evaluation of SPARQL queries over distributed RDF datasets. It uses Sparqlify as a SPARQL-to-SQL rewriter for translating SPARQL queries into Spark executable code. Our preliminary results demonstrate that our approach is more extensible, efficient, and scalable as compared to state-of-the-art approaches. Sparklify is integrated into a larger SANSA framework and it serves as a default query engine and has been used by at least three external use scenarios.
Year
DOI
Venue
2019
10.1007/978-3-030-30796-7_19
Lecture Notes in Computer Science
DocType
Volume
ISSN
Conference
11779
0302-9743
Citations 
PageRank 
References 
1
0.35
0
Authors
4
Name
Order
Citations
PageRank
Claus Stadler136326.65
Gezim Sejdiu2163.33
Damien Graux3104.28
Jens Lehmann45375355.08