  Analysts wishing to explore multivariate data spaces, typically pose queries
involving selection operators, i.e., range or radius queries, which define data
subspaces of possible interest and then use aggregation functions, the results
of which determine their exploratory analytics interests. However, such
aggregate query (AQ) results are simple scalars and as such, convey limited
information about the queried subspaces for exploratory analysis. We address
this shortcoming aiding analysts to explore and understand data subspaces by
contributing a novel explanation mechanism coined XAXA: eXplaining Aggregates
for eXploratory Analytics. XAXA's novel AQ explanations are represented using
functions obtained by a three-fold joint optimization problem. Explanations
assume the form of a set of parametric piecewise-linear functions acquired
through a statistical learning model. A key feature of the proposed solution is
that model training is performed by only monitoring AQs and their answers
on-line. In XAXA, explanations for future AQs can be computed without any
database (DB) access and can be used to further explore the queried data
subspaces, without issuing any more queries to the DB. We evaluate the
explanation accuracy and efficiency of XAXA through theoretically grounded
metrics over real-world and synthetic datasets and query workloads.
