Learn to Explore: on Bootstrapping Interactive Data Exploration with Meta-learning
This work addresses the challenge of efficiently exploring big data for users, though it appears incremental as it builds on active-learning frameworks with a novel meta-learning approach.
The paper tackles the problem of interactive data exploration by framing it as a few-shot learning task, proposing a meta-learning framework that reduces the number of user labeling iterations needed to identify regions of interest in large datasets, with experiments showing improved accuracy and efficiency over existing methods.
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.