한국컴퓨터정보학회논문지 (Journal of the Korea Society of Computer and Information)

  • 제24권8호
  • /
  • Pages.137-142
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  • 2019
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  • 1598-849X(pISSN)
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  • 2383-9945(eISSN)
한국컴퓨터정보학회 (Korean Society of Computer Information)
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Bit-map-based Spatial Data Transmission Scheme

  • OH, Gi Oug (Dept. of Computer Engineering, Gachon University)
  • 투고 : 2019.07.17
  • 심사 : 2019.08.25
  • 발행 : 2019.08.30
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초록

This paper proposed bitmap based spatial data transmission scheme in need of rapid transmission through network in mobile environment that use and creation of data are frequently happen. Former researches that used clustering algorithms, focused on providing service using spatial data can cause delay since it doesn't consider the transmission speed. This paper guaranteed rapid service for user by convert spatial data to bit, leads to more transmission of bit of MTU, the maximum transmission unit. In the experiment, we compared arithmetically default data composed of 16 byte and spatial data converted to bitmap and for simulation, we created virtual data and compared its network transmission speed and conversion time. Virtual data created as standard normal distribution and skewed distribution to compare difference of reading time. The experiment showed that converted bitmap and network transmission are 2.5 and 8 times faster for each.

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참고문헌

  1. J. Schiller, A. Voisard, "Location-Based Services," Elsevier, 2004.
  2. H. t. Hoi, V. H. Ca, L. V. Truong, "Location-Based Services," International Conference on Management, Economics, Business and Social Sciences, ICMEBSS, pp. 81-90, 2018.
  3. M. Kibanov, M. Becker, J. Mueller, M. Atzmueller, A. Hotho, G. Stumme, "Adaptive kNN using expected accuracy for classification of geo-spatial data," Proceedings of the 33rd Annual ACM Symposium on Applied Computing, pp. 857-865, April 2018.
  4. M. Najibi, M. Rastegari, L. S. Davis, "G-CNN: an iterative Grid Based Object Detector," The IEEE Conference on Computer Vision and Pattern Recognition, pp. 2369-2377, June 2016.
  5. Q. Zhao, Y. Shi, Q. Liu, P. Franti, "A grid-growing clustering algorithm for geo-spatial data," Pattern Recognition Letters, Vol. 53, pp. 77-84, 2014. https://doi.org/10.1016/j.patrec.2014.09.017
  6. K. Koperski, J. Han, N. Stefanovic, "An Efficient TwoStep Method for Classication of Spatial Data," SDH '98), Jan. 1999.
  7. Y. Fang, R. Cheng, X. Li, S. Luo, J. Hu, "Effective community search over large spatial graphs," Proceedings of the VLDB Endowment, Vol. 10(6), pp. 709-720, February 2017. https://doi.org/10.14778/3055330.3055337
  8. T. Sajana, C. M. Sheela Rani, K. V. Narayana, "A Survey on Clustering Techniques for Big Data Mining," Indian Journal of Science and Technology, Vol. 9(3), pp. 1-12, January 2016.
  9. A. Eldawy, L. Alarabi, M. F. Mokbel, "Spatial partitioning techniques in SpatialHadoop," International Conference on Very Large Data Bases, Vol. 8(12), August 2015.
  10. H. J. Jang, B. Kim, J. Kim, S. Y. Jung, "An efficient grid-based k-prototypes algorithm for stutainable decision-making on spatial objects," Sustainability, Vol. 10(8), pp. 1-2, 2018. https://doi.org/10.3390/su10020001
  11. A. Guttman, "R-trees: a dynamic index structure for spatial searching," in Proceedings of ACM SIGMOD International Conference on Management of Data, Vol. 14, pp. 47-57, 1984.