Skyline Micro-Cluster Query: A Novel and Practical Spatial Query

This paper presents a novel spatial query, skyline micro-cluster (SMC) query. Given a set of data points P, a query point q, a radius γ and a density parameter k, the SMC query returns the skyline micro-clusters (MCs), where MC is a set of points in P that can be covered by a circle with radius γ and the number of points in MC is at least k. In this paper, we formally define the SMC query. As the brute-force approach to solving the SMC query in massive datasets has high computation and memory costs, we propose a basic skyline micro-cluster query algorithm, BSMC, which can reduce the time complexity from O(2N) to O(N3). Furthermore, on top of BSMC, we propose an efficient skyline micro-cluster query algorithm (ESMC). In ESMC, we use the z-value index and propose a filter to remove the invalid micro-clusters, which reduces significant computation overhead. To reduce the memory overhead, we propose an incremental skyline query method. A comprehensive performance study is conducted on real datasets and the experimental results show that our proposed method, ESMC, can significantly improve the SMC query performance.