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Multiple k nearest neighbor search

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Abstract

The problem of kNN (k Nearest Neighbor) queries has received considerable attention in the database and information retrieval communities. Given a dataset D and a kNN query q, the k nearest neighbor algorithm finds the closest k data points to q. The applications of kNN queries are board, not only in spatio-temporal databases but also in many areas. For example, they can be used in multimedia databases, data mining, scientific databases and video retrieval. The past studies of kNN query processing did not consider the case that the server may receive multiple kNN queries at one time. Their algorithms process queries independently. Thus, the server will be busy with continuously reaccessing the database to obtain the data that have already been acquired. This results in wasting I/O costs and degrading the performance of the whole system. In this paper, we focus on this problem and propose an algorithm named COrrelated kNN query Evaluation (COKE). The main idea of COKE is an “information sharing” strategy whereby the server reuses the query results of previously executed queries for efficiently processing subsequent queries. We conduct a comprehensive set of experiments to analyze the performance of COKE and compare it with the Best-First Search (BFS) algorithm. Empirical studies indicate that COKE outperforms BFS, and achieves lower I/O costs and less running time.

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, Chang Jung Christian University, Taiwan, Republic of China

    Yu-Chi Chung

  2. Department of Computer Science and Information Engineering, National Cheng-Kung University, Taiwan, Republic of China

    I-Fang Su

  3. Department of Computer Science and Information Engineering, National Cheng-Kung University, Taiwan, Republic of China

    Chiang Lee

  4. Cloud Computing Laboratory, Chunghwa Telecom Laboratories, Taipei, Taiwan

    Pei-Chi Liu

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  1. Yu-Chi Chung
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  2. I-Fang Su
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Correspondence to Yu-Chi Chung.

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Chung, YC., Su, IF., Lee, C. et al. Multiple k nearest neighbor search. World Wide Web 20, 371–398 (2017). https://doi.org/10.1007/s11280-016-0392-2

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