Today data is being generated at an unprecedented rate, so much that 90% of the data in the world has been created in the past two years. However, the human ability to comprehend data remains as limited as before. As such, the Big Data era is presenting us with an increasing gap between the growth of data and the 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. To respond to such needs, our team is developing a new database service for interactive exploration in a framework called “explore-by-example.” In this talk, I introduce the explore-by-example framework, which iteratively seeks user relevance feedback on database samples and uses such feedback to finally predict a query that retrieves all objects of interest to the user. The goal is to make such exploration converge fast to the true user interest model, while minimizing the user labeling effort and providing interactive performance. I discuss a range of technical issues to do so for complex user interest patterns. I finally conclude the talk by pointing out a host of new challenges, from application of learning theory, to database optimization, to heterogeneous data sets, to visualization.