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Future of Information and Communication Conference

FICC 2021: Advances in Information and Communication pp 396–414Cite as

Aggregate Nearest Neighborhood Queries

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1364))

Abstract

Efficiency of finding nearest clusters is a principal factor in query processing and data mining, and this can be applied to location information services and services using point of interests (POI). However, no research is currently aimed at finding the nearest cluster of multiple query points. In this paper, we propose Aggregate Nearest Neighborhood Query (ANNH) for searching the nearest cluster of multiple queries with aggregation distance between multiple query points and clusters. Further, one disadvantage is that previous research represents clusters only by circles, and this does not address problems such as biased data points remaining in the cluster. Alternatively, we present a solution to this challenge in ANNH by evaluating cluster density by variance.

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References

  1. Adeniyi, D.A., Wei, Z., Yongquan, Y.: Automated web usage data mining and recommendation system using k-nearest neighbor (kNN) classification method. Appl. Comput. Inform. 12(1), 90–108 (2016)

    Article  Google Scholar 

  2. Bureau, U.C.: Chorochronos.datastories.org. http://chorochronos.datastories.org/?q=node. Accessed 01 Sep 2020

  3. Cheung, K.L., Fu, A.W.C.: Enhanced nearest neighbour search on the R-tree. ACM SIGMOD Rec. 27(3), 16–21 (1998)

    Article  MathSciNet  Google Scholar 

  4. Choi, D., Chung, C.: Nearest neighborhood search in spatial databases. In: 2015 IEEE 31st International Conference on Data Engineering, pp. 699–710 (April 2015)

    Google Scholar 

  5. Deng, K., Sadiq, S., Zhou, X., Xu, H., Fung, G.P.C., Lu, Y.: On group nearest group query processing. IEEE Trans. Knowl. Data Eng. 24(2), 295–308 (2012)

    Article  Google Scholar 

  6. Dong, Y., Chen, H., Yu, J.X., Furuse, K., Kitagawa, H.: Grid-index algorithm for reverse rank queries. In: EDBT, pp. 306–317 (2017)

    Google Scholar 

  7. Guttman, A.: R-trees: a dynamic index structure for spatial searching. In: Proceedings of the 1984 ACM SIGMOD International Conference on Management of Data, pp. 47–57 (1984)

    Google Scholar 

  8. Hjaltason, G.R., Samet, H.: Distance browsing in spatial databases. ACM Trans. Database Syst. (TODS) 24(2), 265–318 (1999)

    Article  Google Scholar 

  9. Le, S., Dong, Y., Chen, H., Furuse, K.: Balanced nearest neighborhood query in spatial database. In: 2019 IEEE International Conference on Big Data and Smart Computing (BigComp), pp. 1–4 (February 2019)

    Google Scholar 

  10. Mouratidis, K., Bakiras, S., Papadias, D.: Continuous monitoring of top-k queries over sliding windows. In: Proceedings of the 2006 ACM SIGMOD International Conference on Management of Data, pp. 635–646 (2006)

    Google Scholar 

  11. Mouratidis, K., Papadias, D., Hadjieleftheriou, M.: Conceptual partitioning: an efficient method for continuous nearest neighbor monitoring. In: Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data, pp. 634–645 (2005)

    Google Scholar 

  12. Papadias, D., Tao, Y., Mouratidis, K., Hui, C.K.: Aggregate nearest neighbor queries in spatial databases. ACM Trans. Database Syst. 30(2), 529–576 (2005). http://doi.acm.org/10.1145/1071610.1071616

    Article  Google Scholar 

  13. Roussopoulos, N., Kelley, S., Vincent, F.: Nearest neighbor queries. In: Proceedings of the 1995 ACM SIGMOD International Conference on Management of Data, pp. 71–79 (1995)

    Google Scholar 

  14. Weber, R., Schek, H.J., Blott, S.: A quantitative analysis and performance study for similarity-search methods in high-dimensional spaces. In: VLDB, vol. 98, pp. 194–205 (1998)

    Google Scholar 

  15. Xiong, X., Mokbel, M.F., Aref, W.G.: SEA-CNN: scalable processing of continuous k-nearest neighbor queries in spatio-temporal databases. In: 21st International Conference on Data Engineering (ICDE 2005), pp. 643–654. IEEE (2005)

    Google Scholar 

  16. Yu, X., Pu, K.Q., Koudas, N.: Monitoring k-nearest neighbor queries over moving objects. In: 21st International Conference on Data Engineering (ICDE 2005), pp. 631–642. IEEE (2005)

    Google Scholar 

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Acknowledgment

This work was supported by JSPS KAKENHI Grant Number JP19K12114.

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Authors and Affiliations

  1. Department of Computer Science, Information and Systems, University of Tsukuba, Tsukuba, Japan

    Hayata Takagi, Hanxiong Chen & Hiroyuki Kitagawa

  2. Faculty of Business Administration, Hakuoh University, Oyama, Japan

    Kazutaka Furuse

Authors
  1. Hayata Takagi
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  2. Hanxiong Chen
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  3. Kazutaka Furuse
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  4. Hiroyuki Kitagawa
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Corresponding author

Correspondence to Hayata Takagi .

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Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

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Takagi, H., Chen, H., Furuse, K., Kitagawa, H. (2021). Aggregate Nearest Neighborhood Queries. In: Arai, K. (eds) Advances in Information and Communication. FICC 2021. Advances in Intelligent Systems and Computing, vol 1364. Springer, Cham. https://doi.org/10.1007/978-3-030-73103-8_28

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