Title: Distributed continuous KNN query over moving objects

Authors: Xiaolin Yang; Zhigang Zhang; Yilin Wang; Cheqing Jin

Addresses: Institute for Data Science and Engineering, School of Computer Science and Software Engineering, East China Normal University, 3663 N. Zhongshan Rd., Shanghai, 200062, China ' Institute for Data Science and Engineering, School of Computer Science and Software Engineering, East China Normal University, 3663 N. Zhongshan Rd., Shanghai, 200062, China ' Institute for Data Science and Engineering, School of Computer Science and Software Engineering, East China Normal University, 3663 N. Zhongshan Rd., Shanghai, 200062, China ' Institute for Data Science and Engineering, School of Computer Science and Software Engineering, East China Normal University, 3663 N. Zhongshan Rd., Shanghai, 200062, China

Abstract: The Continuous K-Nearest Neighbour (CKNN) queries over moving objects have been widely researched in many fields. However, existing centralised works cannot work anymore and distributed solutions suffer the problem of index maintaining, high communication cost and query latency. In this paper, we firstly propose a distributed hybrid indexing strategy which combines the Spatial-temporal Sensitive Grid Index (SSGI) and the dynamic quad-tree index (DQI). The SSGI is proposed to locate the spatial range that contains the final results, and the DQI is used for data partitioning. Then, we introduce an algorithm named HDCKNN to implement the CKNN queries. In comparison of existing work, HDCKNN can achieve the final result in one round iteration, while existing methods require at least two rounds of iteration. Extensive experiments show that the performance of the proposed method is more efficient than state-of-the-art algorithms.

Keywords: moving objects; continuous k-nearest neighbour query; distributed query processing; hybrid indexing.

DOI: 10.1504/IJHPCN.2019.101250

International Journal of High Performance Computing and Networking, 2019 Vol.14 No.2, pp.130 - 138

Received: 15 Oct 2016
Accepted: 23 Feb 2017

Published online: 24 Jul 2019 *

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