Muhammad Saleem
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.
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Digital Library
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- Muhammad Saleem, Intizar Ali, Aidan Hogan, Qaiser Mehmood, and Axel-Cyrille Ngonga Ngomo. 2015. LSQ: The Linked SPARQL Queries Dataset. In ISWC. 261--269.Google Scholar
- Muhammad Saleem, Qaiser Mehmood, and Axel-Cyrille Ngonga Ngomo. 2015. FEASIBLE: a feature-based SPARQL benchmark generation framework. In ISWC. Springer, 52--69. Google ScholarDigital Library
- Muhammad Saleem and Axel-Cyrille Ngonga Ngomo. 2014. HiBISCuS: Hypergraph-Based Source Selection for SPARQL Endpoint Federation. In ESWC.Google Scholar
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Index Terms
- SQCFramework: SPARQL Query Containment Benchmark Generation Framework
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