Regret-Minimizing Representative Databases

We propose the k-representative regret minimization query (k-regret) as an operation to support multi-criteria decision making. Like top-k, the k-regret query assumes that users have some utility or scoring functions; however, it never asks the users to provide such functions. Like skyline, it filters out a set of interesting points from a potentially large database based on the users’ criteria; however, it never overwhelms the users by outputting too many tuples. In particular, for any number k and any class of utility functions, the k-regret query outputs k tuples from the database and tries to minimize the maximum regret ratio. This captures how disappointed a user could be had she seen k representative tuples instead of the whole database. We focus on the class of linear utility functions, which is widely applicable. The first challenge of this approach is that it is not clear if the maximum regret ratio would be small, or even bounded. We answer this question affirmatively. T...

• Type: Journal
• Year: 2010
• Where: PVLDB
• Authors: Danupon Nanongkai, Atish Das Sarma, Ashwin Lall, Richard J. Lipton, Jun Xu
• Keywords: Database, K-regret Query, Maximum Regret Ratio, PVLDB 2010