Active Learning-based Interactive Database Exploration

Data is the new oil, but how to put it to good use is challenging. While the volume of data is growing at an exponential rate, there is an increasing gap between the fast growth of data and the limited human ability to comprehend data. Consequently, there has been a growing demand of data management tools that can bridge this gap and help the user retrieve high-value content from data more effectively. The goal of our work is to build an interactive data exploration system as a new database service, using an approach called “explore-by-example”. In particular, we cast the explore-by-example problem in a principled “active learning” framework, and bring the properties of important classes of database queries to bear on the design of new algorithms and optimizations for active learning-based database exploration. In this talk, we will present our proposed new techniques that enable the user to complete data exploration tasks with the least possible labeling effort and improve accuracy, efficiency and scalability for active learning-based interactive database exploration.