Document Type

Presentation

Publication Date

September 2010

Abstract

Relational database systems are becoming increasingly popular in the scientific community to support the interactive exploration of large volumes of data. In this scenario, users employ a query interface (typically, a web-based client) to issue a series of SQL queries that aim to analyze the data and mine it for interesting information. First-time users, however, may not have the necessary knowledge to know where to start their exploration. Other times, users may simply overlook queries that retrieve important information. In this work we describe a framework to assist non-expert users by providing personalized query recommendations. The querying behavior of the active user is represented by a set of query fragments, which are then used to identify similar query fragments in the recorded sessions of other users. The identified fragments are then transformed to interesting queries that are recommended to the active user. An experimental evaluation using real user traces shows that the generated recommendations can achieve high accuracy.

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