Automatic Categorization of Web Database Query Results

Web database queries are often exploratory. The users often find that their queries return too many answers and many of them may be irrelevant. Based on different kinds of user preferences, this chapter proposes a novel categorization approach which consists of two steps. The first step analyzes query history of all users in the system offline and generates a set of clusters over the tuples, where each cluster represents one type of user preference. When a user issues a query, the second step presents to the user a category tree over the clusters generated in the first step such that the user can easily select the subset of query results matching his needs. The problem of constructing a category tree is a cost optimization problem and heuristic algorithms were developed to compute the min-cost categorization. The efficiency and effectiveness of our approach are demonstrated by experimental results.