  Selectivity estimation is important in query optimization, however accurate
estimation is difficult when predicates are complex. Instead of existing
database synopses and statistics not helpful for such cases, we introduce a new
approach to compute the exact selectivity by running an aggregate query during
the optimization phase. Exact selectivity can be achieved without significant
overhead for in-memory and GPU-accelerated databases by adding extra query
execution calls. We implement a selection push-down extension based on the
novel selectivity estimation strategy in the MapD database system. Our approach
records constant and less than 30 millisecond overheads in any circumstances
while running on GPU. The novel strategy successfully generates better query
execution plans which result in performance improvement up to 4.8 times from
TPC-H benchmark SF-50 queries and 7.3 times from star schema benchmark SF-80
queries.
