  The present state of the art in analytics requires high upfront investment of
human effort and computational resources to curate datasets, even before the
first query is posed. So-called pay-as-you-go data curation techniques allow
these high costs to be spread out, first by enabling queries over uncertain and
incomplete data, and then by assessing the quality of the query results. We
describe the design of a system, called Mimir, around a recently introduced
class of probabilistic pay-as-you-go data cleaning operators called Lenses.
Mimir wraps around any deterministic database engine using JDBC, extending it
with support for probabilistic query processing. Queries processed through
Mimir produce uncertainty-annotated result cursors that allow client
applications to quickly assess result quality and provenance. We also present a
GUI that provides analysts with an interactive tool for exploring the
uncertainty exposed by the system. Finally, we present optimizations that make
Lenses scalable, and validate this claim through experimental evidence.
