  In this paper, we argue that database systems be augmented with an automated
data exploration service that methodically steers users through the data in a
meaningful way. Such an automated system is crucial for deriving insights from
complex datasets found in many big data applications such as scientific and
healthcare applications as well as for reducing the human effort of data
exploration. Towards this end, we present AIDE, an Automatic Interactive Data
Exploration framework that assists users in discovering new interesting data
patterns and eliminate expensive ad-hoc exploratory queries.
  AIDE relies on a seamless integration of classification algorithms and data
management optimization techniques that collectively strive to accurately learn
the user interests based on his relevance feedback on strategically collected
samples. We present a number of exploration techniques as well as optimizations
that minimize the number of samples presented to the user while offering
interactive performance. AIDE can deliver highly accurate query predictions for
very common conjunctive queries with small user effort while, given a
reasonable number of samples, it can predict with high accuracy complex
disjunctive queries. It provides interactive performance as it limits the user
wait time per iteration of exploration to less than a few seconds.
