There is an increasing gap between fast growth of data and 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 e↵ectively. In this work, we aim to build interactive data exploration 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. These new techniques allow the database system to overcome a fundamental limitation of traditional active learning, i.e., the slow convergence problem. Evaluation results using real-world datasets and user interest patterns show that our new system significantly outperforms state-of-the-art active learning techniques and data exploration systems in accuracy while achieving desired eciency for interactive performance.