Efficient processing of k-regret minimization queries with theoretical guarantees

Assisting end users to identify desired results from a large dataset is an important problem for multi-criteria decision making. To address this problem, top-k and skyline queries have been widely adopted, but they both inherent drawbacks, i.e., the user either has provide specific utility function or faces many results. The k-regret minimization query proposed, which integrates merits of queries. Due NP-hardness time consuming greedy framework adopted. However, formal theoretical analysis approaches quality returned still lacking. In paper, we first fill gap by conducting nontrivial approximation ratio speed up processing, sampling-based method, StocPresGreed, developed reduce evaluation cost. addition, required sample size conducted bound Finally, comprehensive experiments are on real synthetic datasets demonstrate efficiency effectiveness proposed methods.