Efficient Maintenance of k-Dominant Skyline for Frequently Updated Database

Abstract

Skyline queries retrieve a set of skyline objects so that the user can choose promising objects from them and make further inquiries. However, a skyline query often retrieves too many objects to analyze intensively. To solve the problem, k-dominant skyline queries have been introduced, which can reduce the number of retrieved objects by relaxing the definition of the dominance. Though it can reduce the number of retrieved objects, the k-dominant skyline objects are difficult to maintain if the database is updated. This paper addresses the problem of maintenance of k-dominant skyline objects of frequently updated database. We propose an algorithm for maintaining k-dominant skyline objects. Intensive experiments using real and synthetic datasets demonstrated that our method is efficient and scalable.