The Data Interaction Game.

As many users do not precisely know the structure and/or the content of databases, their queries do not exactly reflect their information needs. The database management systems (DBMS) may interact with users and leverage their feedback on the returned results to learn the information needs behind users' queries. Current query interfaces assume that users follow a fixed strategy of expressing their information needs, that is, the likelihood by which a user submits a query to express an information need remains unchanged during her interaction with the DBMS. Using a real-world interaction workload, we show that users learn and modify how to express their information needs during their interactions with the DBMS. We also show that users' learning is accurately modeled by a well-known reinforcement learning mechanism. As current data interaction systems assume that users do not modify their strategies, they cannot discover the information needs behind users' queries effectively. We model the interaction between users and DBMS as a game with identical interest between two rational agents whose goal is to establish a common language for representing information needs in form of queries. We propose a reinforcement learning method that learns and answers the information needs behind queries and adapts to the changes in users' strategies and prove that it improves the effectiveness of answering queries stochastically speaking. We analyze the challenges of efficient implementation of this method over large-scale relational databases and propose two efficient adaptations of this algorithm over large-scale relational databases. Our extensive empirical studies over real-world query workloads and large-scale relational databases indicate that our algorithms are efficient. Our empirical results also show that our proposed learning mechanism is more effective than the state-of-the-art query answering method.