  Selectivity estimation - the problem of estimating the result size of queries
- is a fundamental problem in databases. Accurate estimation of query
selectivity involving multiple correlated attributes is especially challenging.
Poor cardinality estimates could result in the selection of bad plans by the
query optimizer. We investigate the feasibility of using deep learning based
approaches for both point and range queries and propose two complementary
approaches. Our first approach considers selectivity as an unsupervised deep
density estimation problem. We successfully introduce techniques from neural
density estimation for this purpose. The key idea is to decompose the joint
distribution into a set of tractable conditional probability distributions such
that they satisfy the autoregressive property. Our second approach formulates
selectivity estimation as a supervised deep learning problem that predicts the
selectivity of a given query. We also introduce and address a number of
practical challenges arising when adapting deep learning for relational data.
These include query/data featurization, incorporating query workload
information in a deep learning framework and the dynamic scenario where both
data and workload queries could be updated. Our extensive experiments with a
special emphasis on queries with a large number of predicates and/or small
result sizes demonstrates that our proposed techniques provide fast and
accurate selective estimates with minimal space overhead.
