  The optimization of query execution plans is known to be crucial for reducing
the query execution time. In particular, query optimization has been studied
thoroughly for relational databases over the past decades. Recently, the
Resource Description Framework (RDF) became popular for publishing data on the
Web. As a consequence, federations composed of different data models like RDF
and relational databases evolved. One type of these federations are Semantic
Data Lakes where every data source is kept in its original data model and
semantically annotated with ontologies or controlled vocabularies. However,
state-of-the-art query engines for federated query processing over Semantic
Data Lakes often rely on optimization techniques tailored for RDF. In this
paper, we present query optimization techniques guided by heuristics that take
the physical design of a Data Lake into account. The heuristics are implemented
on top of Ontario, a SPARQL query engine for Semantic Data Lakes. Using
source-specific heuristics, the query engine is able to generate more efficient
query execution plans by exploiting the knowledge about indexes and
normalization in relational databases. We show that heuristics which take the
physical design of the Data Lake into account are able to speed up query
processing.
