User-driven refinement of imprecise queries

With the advent of big data everywhere, the need for new techniques for exploratory search in large databases is magnified. The focus of this work is on less technical users who often query a database through a limited interface. We propose user-driven query refinement as an interactive process to aid users create and refine query conditions. This process is characterized by three key challenges: (1) dealing with incomplete and imprecise query conditions, (2) keeping user effort low, and (3) guaranteeing interactive system response time. We address the first two challenges with a probability-based framework that guides the user to the most important query conditions. To recover from input errors, we introduce the notion of sensitivity and propose efficient algorithms for identifying the most sensitive user-specified query conditions, i.e., those conditions that had the greatest influence on the query results. For the third challenge, we develop techniques to estimate the required probabilities within a given hard realtime limit.