Abstract
The paper proposes an optimal distributed k Nearest Neighbor query processing algorithm based on Data Grid, called the opGkNN. Three steps are incorporated in the opGkNN. First when a user submits a query with a vector Vq and a number k, an iDistance[3]-based vector set reduction is first conducted at data node level in parallel. Then the candidate vectors are transferred to the executing nodes for the refinement process in which the answer set is obtained. Finally, the answer set is transferred to the query node. The experimental results show that the performance of the algorithm is efficient and effective in minimizing the response time by decreasing network transfer cost and increasing the parallelism of I/O and CPU.
This paper is partially supported by the Program of National Natural Science Foundation of China under Grant No. 60873022; The Program of Natural Science Foundation of Zhejiang Province under Grant No. Y1080148; The Key Program of Science and Technology of Zhejiang Province under Grant No. 2008C13082; The Open Project of Zhejiang Provincial Key Laboratory of Information Network Technology; The Key Project of Special Foundation for Young Scientists in Zhejiang Gongshang University.
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Zhuang, Y., Hu, H., Li, X., Xu, B., Hu, H. (2009). Optimal K-Nearest-Neighbor Query in Data Grid. In: Li, Q., Feng, L., Pei, J., Wang, S.X., Zhou, X., Zhu, QM. (eds) Advances in Data and Web Management. APWeb WAIM 2009 2009. Lecture Notes in Computer Science, vol 5446. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00672-2_53
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DOI: https://doi.org/10.1007/978-3-642-00672-2_53
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-00671-5
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