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An evaluation and query algorithm for the influence of spatial location based on RkNN

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Abstract

This paper is devoted to the investigation of the evaluation and query algorithm problem for the influence of spatial location based on RkNN (reverse k nearest neighbor). On the one hand, an object can make contribution to multiple locations. However, for the existing measures for evaluating the influence of spatial location, an object only makes contribution to one location, and its influence is usually measured by the number of spatial objects in the region. In this case, a new measure for evaluating the influence of spatial location based on the RkNN is proposed. Since the weight of the contribution is determined by the distance between the object and the location, the influence weight definition is given, which meets the actual applications. On the other hand, a query algorithm for the influence of spatial location is introduced based on the proposed measure. Firstly, an algorithm named INCH (INtersection’s Convex Hull) is applied to get candidate regions, where all objects are candidates. Then, kNN and Range-k are used to refine results. Then, according to the proposed measure, the weights of objects in RkNN results are computed, and the influence of the location is accumulated. The experimental results on the real data show that the optimized algorithms outperform the basic algorithm on efficiency. In addition, in order to provide the best customer service in the location problem and make the best use of all infrastructures, a location algorithm with the query is presented based on RkNN. The influence of each facility is calculated in the location program and the equilibrium coefficient is used to evaluate the reasonability of the location in the paper. The smaller the equilibrium coefficient is, the more reasonability the program is. The actual application shows that the location based on influence makes the location algorithm more reasonable and available.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grants Nos. 61602323, 61703288), Natural Science Foundation of Liaoning Province (2019-MS-264, 201602604), and Technology Research Project of Education Department of Liaoning (lnqn201913).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Northeastern University, Shenyang, 110819, China

    Jingke Xu & Ge Yu

  2. School of Information and Control Engineering, Shenyang Jianzhu University, Shenyang, 110168, China

    Jingke Xu & Yidan Zhao

  3. Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction, Shenyang, 110168, China

    Jingke Xu

Authors
  1. Jingke Xu
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  2. Yidan Zhao
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  3. Ge Yu
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Correspondence to Jingke Xu.

Additional information

Jingke Xu received ME degree in Computer Software and Theory from Northeastern University, China and he is currently a PhD student at Northeastern University, China.

He is an associate professor of computer science at Shenyang Jianzhu University, China. His main research interests include spatiotemporal data management and analysis, data mining, and intelligent optimization, etc.

Dr. XU is a CCF member and has hosted and participated in a number of national and provincial scientific research projects. He has published more than 20 papers in important journals and conferences at home and abroad, such as Journal of Computer Research and Development, Chinese Journal of Computers and NDBC, etc.

Yidan Zhao received BE degree in school of Computer Science and Technology from Shenyang Jianzhu University, China in 2017. Currently, she is a master graduate student of Shenyang Jianzhu University, China.

Her main research interests include big data management and analysis and data mining. She has published 2 papers in important journals and conferences.

Ge Yu received his BE degree and ME degree in Computer Science from Northeastern University of China in 1982 and 1986, respectively, PhD degree in Computer Science from Kyushu University of Japan in 1996.

He has been a full professor at Northeastern University of China since 1996. His current research interesting includes database theory and technology, distributed and parallel systems, cloud computing and big data management, blockchain techniques and their applications.

Prof. YU is a CCF fellow, an IEEE senior member, an ACM member, and the associate editor of Chinese Journal of Computers, Journal of Software, and Journal of Computer Research and Development.

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Xu, J., Zhao, Y. & Yu, G. An evaluation and query algorithm for the influence of spatial location based on RkNN. Front. Comput. Sci. 15, 152604 (2021). https://doi.org/10.1007/s11704-020-9238-2

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  • DOI: https://doi.org/10.1007/s11704-020-9238-2

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