  Interactive data exploration (IDE) is an effective way of comprehending big
data, whose volume and complexity are beyond human abilities. The main goal of
IDE is to discover user interest regions from a database through multi-rounds
of user labelling. Existing IDEs adopt active-learning framework, where users
iteratively discriminate or label the interestingness of selected tuples. The
process of data exploration can be viewed as the process of training a
classifier, which determines whether a database tuple is interesting to a user.
An efficient exploration thus takes very few iterations of user labelling to
reach the data region of interest. In this work, we consider the data
exploration as the process of few-shot learning, where the classifier is
learned with only a few training examples, or exploration iterations. To this
end, we propose a learning-to-explore framework, based on meta-learning, which
learns how to learn a classifier with automatically generated meta-tasks, so
that the exploration process can be much shortened. Extensive experiments on
real datasets show that our proposal outperforms existing explore-by-example
solutions in terms of accuracy and efficiency.
