2018 IEEE 34th International Conference on Data Engineering (ICDE) (2018)
Apr 16, 2018 to Apr 19, 2018
Most users do not know the structure and content of databases or concepts such as schema or formal query languages sufficiently well to express their information needs precisely in form of queries. They may convey their intents via usable but inherently ambiguous query interfaces, such as keyword or natural language queries, which are open to numerous interpretations. Thus, it is very challenging for a database management system (DBMS) to understand and satisfy the intents behind these queries. A useful source of information to decode the intent behind a query is the user feedback on the returned results, which may come in various ways, such as click-through information. To use this information, DBMS has to establish a trade-off between showing strongly reinforced answers, i.e., exploitation, and exploring never-before-shown or rarely shown answers to solicit user feedback on them, i.e. exploration. A DBMS that returns only positively reinforced results may always return a small subset of all relevant answers to a query. On the other hand, if a DBMS shows too many unseen answers to the user, it may discourage and disengage users as many such answers may not be relevant to the query. Moreover, users may learn more about the structure and content of the database and how to express intents as they submit queries and observe the returned results. Thus, they may modify the way they express intents over the course of their interactions with the DBMS.
Game theory, information retrieval, reinforcement learning, database interaction
B. McCamish, A. Termehchy and B. Touri, "The Data Exploration Game," 2018 IEEE 34th International Conference on Data Engineering (ICDE), Paris, France, 2018, pp. 1668-1669.