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SQCFramework: SPARQL Query Containment Benchmark Generation Framework

 
: Saleem, Muhammad; Stadler, Claus; Mehmood, Qaiser; Lehmann, Jens; Ngonga Ngomo, Axel-Cyrille

:
Preprint ()

Association for Computing Machinery -ACM-:
K-CAP 2017. Proceedings of the Knowledge Capture Conference : Austin, TX, USA, December 04 - 06, 2017
New York: ACM, 2017
ISBN: 978-1-4503-5553-7
Article 28, 8 S.
International Conference on Knowledge Capture (K-CAP) <9, 2017, Austin/Tex.>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IAIS ()

Abstract
Query containment is a fundamental problem in data management with its main application being in global query optimization. A number of SPARQL query containment solvers for SPARQL have been recently developed. To the best of our knowledge, the Query Containment Benchmark (QC-Bench) is the only benchmark for evaluating these containment solvers. However, this benchmark contains a fixed number of synthetic queries, which were handcrafted by its creators. We propose SQCFramework, a SPARQL query containment benchmark generation framework which is able to generate customized SPARQL containment benchmarks from real SPARQL query logs. The framework is flexible enough to generate benchmarks of varying sizes and according to the user-defined criteria on the most important SPARQL features to be considered for query containment benchmarking. This is achieved using different clustering algorithms. We compare state-of-the-art SPARQL query containment solvers by using different query containment benchmarks generated from DBpedia and Semantic Web Dog Food query logs. In addition, we analyze the quality of the different benchmarks generated by SQCFramework.

: http://publica.fraunhofer.de/dokumente/N-481438.html