Guided SQL-Based Data Exploration with User Feedback

The exploration of large, real-world databases poses major challenges to users due to their volume and complexity. SQL is the preferred language for data exploration. However, the process of iteratively refining SQL queries is tedious and time consuming. We formulate the automation of personalized SQL-based data exploration as the problem of suggesting the most relevant query and accounting for user feedback at each step. We develop an end-to-end solution and a system to assist users in exploring different components of a complex database. We instantiate our solution using Multi-Armed Bandits, a category of algorithms that are suitable for interactive online learning by balancing exploration with exploitation. We design a lightweight algorithm to personalize stepwise SQL recommendations that efficiently discovers the current user preferences in coordination with that user's feedback and what other users prefer. We run extensive experiments that demonstrate the utility of our approach for large-scale data exploration.