Progressive Approaches for Pareto Optimal Groups Computation

Abstract

Group skyline query is a powerful tool for optimal group analysis. Most of the existing group skyline queries select optimal groups by comparing the dominance relationship between aggregate-based points; such feature creates difficulties for users to specify an appropriate aggregate function. Besides, many significant groups that have great attractions to users in practice may be overlooked. To address these issues, the group skyline (GSky) query is formulated on the basis of a general definition of group dominance operator. While the existing GSky query algorithms are effective, there is still room for improvement in terms of progressiveness and efficiency. In this paper, we propose some new lemmas which facilitate direct generation of the GSky query results. Consecutively, we design a layered unit-based (LU) algorithm that applies a layered optimum strategy. Additionally, for the GSky query over the data that are dynamically produced and cannot be indexed, we propose a novel index-independent algorithm, called sorted-based progressive (SP) algorithm. The experimental results demonstrate the effectiveness, efficiency, and progressiveness of the proposed algorithms. By comparing with the state-of-the-art algorithm for the GSky query, our LU algorithm is more scalable and two orders of magnitude faster.