An efficient index structure for distributed k-nearest neighbours query processing
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Many location-based services are supported by the moving k-nearest neighbour (k-NN) query, which continuously returns the k-nearest data objects for a query point. Most of existing approaches to this problem have focused on a centralized setting, which show poor scalability to work around massive-scale and distributed data sets. In this paper, we propose an efficient distributed solution for k-NN query over moving objects to tackle the increasingly large scale of data. This approach includes a new grid-based index called Block Grid Index (BGI), and a distributed k-NN query algorithm based on BGI. There are three advantages of our approach: (1) BGI can be easily constructed and maintained in a distributed setting; (2) the algorithm is able to return the results set in only two iterations. (3) the efficiency of k-NN query is improved. The efficiency of our solution is verified by extensive experiments with millions of nodes.
Keywordsk-Nearest neighbour query Distributed query processing Moving objects
This work was supported in part by the 973 Program (2015CB352500), the National Natural Science Foundation of China Grant (61272092), the Shandong Provincial Natural Science Foundation Grant (ZR2012FZ004), the Science and Technology Development Program of Shandong Province (2014G GE27178), the Taishan Scholars Program and NSERC Discovery Grants.
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Conflict of interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
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