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Using Clusters for Approximate Continuous k-Nearest Neighbour Queries

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Advanced Information Networking and Applications (AINA 2021)

Abstract

This paper proposes several strategies for continuous k-nearest neighbour query processing in location-based services. All utilize a clustered dataset to create safe regions. The only previously proposed strategy to do so has limited validation of the safe region on the client, which leads to only approximate k-nearest neighbour results being obtained. This work improves upon both the creation of the safe region and the validation of the safe region on the client. An evaluation of our strategy, including a comparison versus the existing strategy, show significant improvements in accuracy, and up to over 90% accuracy. When compared to repeated k-nearest neighbour search, our strategy is computationally significantly faster for larger datasets. Obtaining significantly high accuracy at low computational costs is significant for our strategies.

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Notes

  1. 1.

    http://www.cs.waikato.ac.nz/ml/weka.

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Correspondence to Wendy Osborn .

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Osborn, W. (2021). Using Clusters for Approximate Continuous k-Nearest Neighbour Queries. In: Barolli, L., Woungang, I., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2021. Lecture Notes in Networks and Systems, vol 225. Springer, Cham. https://doi.org/10.1007/978-3-030-75100-5_40

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