The application of Semantic Web technologies in an Electronic Commerce environment implies a need for good support tools. Fast query engines are required for efficient real-time querying of large amounts of data, usually represented using RDF. We focus on optimizing a special class of SPARQL queries: RDF chain queries. We devise a genetic algorithm, RCQ-GA, that determines the order in which joins need to be performed for an efficient evaluation of RDF chain queries. The approach is benchmarked against a two-phase optimization algorithm, previously proposed in literature. The more complex a query is, the more RCQ-GA outperforms the benchmark in solution quality, execution time needed, and consistency of solution quality. When the algorithms are constrained by a time limit, the overall performance of RCQ-GA compared to the benchmark improves even further.
[1]
A. E. Eiben,et al.
Parameter calibration using meta-algorithms
,
2007,
2007 IEEE Congress on Evolutionary Computation.
[2]
Uzay Kaymak,et al.
QMap : an RDF-based queryable world map
,
2008
.
[3]
Guido Moerkotte,et al.
Heuristic and randomized optimization for the join ordering problem
,
1997,
The VLDB Journal.
[4]
Theodore W. Manikas,et al.
Genetic Algorithms vs. Simulated Annealing: A Comparison of Approaches for Solving the Circuit Partitioning Problem
,
1996
.
[5]
Heiner Stuckenschmidt,et al.
Towards distributed processing of RDF path queries
,
2005,
Int. J. Web Eng. Technol..