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The optimization of query execution plans is known to be crucial for reducingthe query execution time. In particular, query optimization has been studiedthoroughly for relational databases over the past decades. Recently, theResource Description Framework (RDF) became popular for publishing data on theWeb. As a consequence, federations composed of different data models like RDFand relational databases evolved. One type of these federations are SemanticData Lakes where every data source is kept in its original data model andsemantically annotated with ontologies or controlled vocabularies. However,state-of-the-art query engines for federated query processing over SemanticData Lakes often rely on optimization techniques tailored for RDF. In thispaper, we present query optimization techniques guided by heuristics that takethe physical design of a Data Lake into account. The heuristics are implementedon top of Ontario, a SPARQL query engine for Semantic Data Lakes. Usingsource-specific heuristics, the query engine is able to generate more efficientquery execution plans by exploiting the knowledge about indexes andnormalization in relational databases. We show that heuristics which take thephysical design of the Data Lake into account are able to speed up queryprocessing.

Optimizing Federated Queries Based on the Physical Design of a Data Lake