Continuous k-Regret Minimization Queries: A Dynamic Coreset Approach

Finding a small set of representative tuples from a large database is an important functionality for supporting multi-criteria decision making. Top-k k queries and skyline queries are two widely studied queries to fulfill this task. However, both of them have some limitations: a top-k k query requires the user to provide her utility functions for finding the k k tuples with the highest scores as the result; a skyline query does not need any user-specified utility function but cannot control the result size. To overcome their drawbacks, the k k -regret minimization query was proposed and received much attention recently, since it does not require any user-specified utility function and returns a fixed-size result set. Specifically, it selects a set R R of tuples with a pre-defined size r r from a database D D such that the maximum k-regret ratio , which captures how well the top-ranked tuple in R R represents the top-k k tuples in