Cost-Driven Ontology-Based Data Access

Davide Lanti, Guohui Xiao, and Diego Calvanese

Proc. of the 16th Int. Semantic Web Conf. (ISWC 2017). Volume 10587 of Lecture Notes in Computer Science. 2017.

SPARQL query answering in ontology-based data access (OBDA) is carried out by translating into SQL queries over the data source. Standard translation techniques try to transform the user query into a union of conjunctive queries (UCQ), following the heuristic argument that UCQs can be efficiently evaluated by modern relational database engines. In this work, we show that translating to UCQs is not always the best choice, and that, under certain conditions on the interplay between the ontology, the mappings, and the statistics of the data, alternative translations can be evaluated much more efficiently. To find the best translation, we devise a cost model together with a novel cardinality estimation that takes into account all such OBDA components. Our experiments confirm that (i) alternatives to the UCQ translation might produce queries that are orders of magnitude more efficient, and (ii) the cost model we propose is faithful to the actual query evaluation cost, and hence is well suited to select the best translation.

   title = "Cost-Driven Ontology-Based Data Access",
   year = "2017",
   author = "Davide Lanti and Guohui Xiao and Diego Calvanese",
   booktitle = "Proc. of the 16th Int. Semantic Web Conf. (ISWC 2017)",
   pages = "452--470",
   volume = "10587",
   publisher = "Springer",
   series = "Lecture Notes in Computer Science",
   doi = "10.1007/978-3-319-68288-4_27",
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