Kaushik Chakrabarti, Surajit Chaudhuri, and Seung-won Hwang
Exploratory ad-hoc queries could return too many answers – a phenomenon commonly referred to as “information overload”. In this paper, we propose to automatically categorize the results of SQL queries to address this problem. We dynamically generate a labeled, hierarchical category structure – users can determine whether a category is relevant or not by examining simply its label; she can then explore just the relevant categories and ignore the remaining ones, thereby reducing information overload. We first develop analytical models to estimate information overload faced by a user for a given exploration. Based on those models, we formulate the categorization problem as a cost optimization problem and develop heuristic algorithms to compute the min-cost categorization.
|Published in||ACM SIGMOD Conference|