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Continuous k-Nearest Neighbour Strategies Using the mqrtree

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Advances in Network-Based Information Systems (NBiS 2018)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 22))

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Abstract

In this paper, two strategies for processing a continuous k-nearest neighbor query for location-based services are proposed. Both use a spatial access method, the mqrtree, for locating a safe region. The mqrtree supports searching within the structure, so searches from the root are not required - a property which is exploited in the strategies. However, the proposed strategies will work with most spatial access methods. The strategies are evaluated and compared against a repeated nearest neighbor search. It is shown that both approaches achieve significant performance gains in reducing the number of times a new safe region must be identified, in both random and exponentially distributed points sets.

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Authors and Affiliations

  1. Department of Mathematics and Computer Science, University of Lethbridge, Lethbridge, AB, T1K 3M4, Canada

    Wendy Osborn

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  1. Wendy Osborn
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Correspondence to Wendy Osborn .

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Editors and Affiliations

  1. Department of Information and Communication Engineering, Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Faculty of Management, Comenius University in Bratislava, Bratislava, Slovakia

    Natalia Kryvinska

  3. Faculty of Business Administration, Rissho University, Tokyo, Japan

    Tomoya Enokido

  4. Department of Advanced Sciences, Hosei University, Tokyo, Japan

    Makoto Takizawa

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Osborn, W. (2019). Continuous k-Nearest Neighbour Strategies Using the mqrtree. In: Barolli, L., Kryvinska, N., Enokido, T., Takizawa, M. (eds) Advances in Network-Based Information Systems. NBiS 2018. Lecture Notes on Data Engineering and Communications Technologies, vol 22. Springer, Cham. https://doi.org/10.1007/978-3-319-98530-5_15

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