Provenance has become increasingly important in scientific workflows to understand, verify,
and reproduce the result of scientific data analysis. Most existing systems store provenance
data in provenance stores with proprietary provenance data models and conduct query
processing over the physical provenance storages using query languages, such as SQL,
SPARQL, and XQuery, which are closely coupled to the underlying storage strategies.
Querying provenance at such low level leads to poor usability of the system: a user needs to …