Learn to Explore: on Bootstrapping Interactive Data Exploration with Meta-learning

TL;DR

This paper introduces a meta-learning framework for interactive data exploration that significantly reduces the number of user interactions needed to identify interesting data regions, outperforming existing methods in accuracy and efficiency.

Contribution

It proposes a novel meta-learning approach to improve few-shot classifier training for interactive data exploration, enabling faster and more accurate data region discovery.

Findings

Outperforms existing explore-by-example methods in accuracy.

Reduces number of user interactions required for effective exploration.

Demonstrates effectiveness on real datasets.

Abstract

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.