A Signaling Game Approach to Databases Querying and Interaction
Ben McCamish, Vahid Ghadakchi, Arash Termehchy, Behrouz Touri

TL;DR
This paper models user-DBMS interactions as a signaling game, using reinforcement learning to adapt to users' evolving query strategies, thereby improving query understanding and response accuracy in real-world database systems.
Contribution
It introduces a novel game-theoretic framework with reinforcement learning for dynamic query interpretation, addressing limitations of existing static interaction models.
Findings
Reinforcement learning effectively models user strategy changes.
The proposed algorithms improve query answering accuracy.
Empirical results show efficiency and effectiveness on real workloads.
Abstract
As most 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 use their feedback on the returned results to learn the information needs behind their queries. Current query interfaces assume that users do not learn and modify the way way they express their information needs in form of queries during their 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 and their 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…
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Taxonomy
TopicsData Stream Mining Techniques · Peer-to-Peer Network Technologies · Advanced Database Systems and Queries
