Finding the informative and concise set through approximate skyline queries

Abstract

Querying databases to search for “best” objects matching users’ preferences is a fundamental problem of intelligent systems and applications. The skyline query is an important tool for solving such a best-matching problem from the concept of multi-criteria optimization. However, it has the size problem as the size of the results of a skyline query grows superlinearly with the number of criteria. Here, we propose to find both the informative and concise set of skyline, a refined skyline set without similar objects. The informativeness requires the reduced set to cover the skyline, i.e., for every skyline point in the original dataset, there exists a close point in the reduced set, which help users to understand the skyline in more detail and make a better decision. The conciseness requires the size of the reduced set that can cover the skyline is as smaller as possible, which help users to make a quick decision. Finding both the informative and concise set of skyline will boost the usability of intelligent systems. More specifically, we propose two new skyline queries to find the informative and concise set of skyline: minimum skyline query, and extended minimum skyline query. The main idea is to return the minimum number of approximation objects, in which there is an object within a predefined distance threshold for each skyline object. The difference of the two query types is that the former one selects approximation objects only from skyline set, while the latter one selects from the whole dataset. We present an exact solution which computes minimum skyline in linear time for a 2d-space. As both minimum skyline and extended minimum skyline problems are NP-hard for dimensionality at least three, we present greedy solutions that obtain a 1+lnR approximation of the optimums. A comprehensive performance evaluation demonstrates that the size of skyline set can be effectively reduced by using the proposed (extended) minimum skyline queries and our proposed algorithms have promising results.