Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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An Advanced Scheme for Searching Spatial Objects and Identifying Hidden Objects

숨은 객체 식별을 위한 향상된 공간객체 탐색기법

  • Kim, Jongwan (Department of Computer Engineering, Sungkyul University) ;
  • Cho, Yang-Hyun (Division of Computer Science, Sahmyook University)
  • Received : 2014.05.18
  • Accepted : 2014.06.09
  • Published : 2014.07.31
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Abstract

In this paper, a new method of spatial query, which is called Surround Search (SuSe) is suggested. This method makes it possible to search for the closest spatial object of interest to the user from a query point. SuSe is differentiated from the existing spatial object query schemes, because it locates the closest spatial object of interest around the query point. While SuSe searches the surroundings, the spatial object is saved on an R-tree, and MINDIST, the distance between the query location and objects, is measured by considering an angle that the existing spatial object query methods have not previously considered. The angle between targeted-search objects is found from a query point that is hidden behind another object in order to distinguish hidden objects from them. The distinct feature of this proposed scheme is that it can search the faraway or hidden objects, in contrast to the existing method. SuSe is able to search for spatial objects more precisely, and users can be confident that this scheme will have superior performance to its predecessor.

본 논문은 주변탐색(Surrounder Search: SuSe)이라는 새로운 공간질의 방법을 제안한다. 이 기법은 현재 사용자의 위치를 중심으로 주변에서 가까운 관심영역의 공간객체를 탐색하는 것이다. 사용자 중심의 주변탐색은 증강현실과 같이 사용자가 관심 있어 하는 공간객체 중 가까운 것을 찾기 때문에 기존의 공간질의와 구별된다. 기존 기법은 질의점과 객체 사이의 최단거리(MINDIST)를 기준으로 주변을 탐색하지만 제안 기법에서는 객체들 사이에 숨어있지만 관심의 대상인 숨은 객체를 식별하기 위해서 각도(Angle)를 함께 고려하여 탐색한다. 제안 기법의 특징은 기존기법이 거리만을 사용하여 가까운 객체를 탐색한 것과 달리 거리는 멀지만 숨은 객체까지도 찾아냄으로써 사용자의 선호도를 더 세밀하게 반영한다. 실험결과에서 제안기법인 SuSe는 최근접 이웃 탐색기법인 NN(Nearest Neighbor)과 비교하여 보다 정밀한 공간객체 탐색이 가능하며 향상된 탐색성능을 타나낸다.

Keywords

References

  1. J. Kim, S. J. Im, S. W. Kang, C. S. Hwang, S. K. Lee, "SQR-tree : A Spatial Index Using Semi-quantized MBR Compression Scheme in R-tree", Journal of Information Science and Engineering(JISE), Vol. 23, No. 5, pp. 154-156, 2007.
  2. H. S. Joo, J. W. Kim, "Spatial Data Compression for Location-Based Game", ICCC2010 International Conference on Convergence Contents, pp. 365-366, Dec. 2010.
  3. O. S. Park, Jung, K. R., Kim, S. H., "Location Sensing Tech. and System for Ubiquitous Computing," Weekly Technical Trend, Vol. 1098, pp. 11-21, 2003.
  4. K. Y. Lee, D. O. Kim, "Design of a Location Management System in the Ubiquitous Computing Environments," Journal of KIISE, Vol.12, No.6, pp.115-121, Dec. 2007.
  5. M. Weiser, "Ubiquitous Computing," ACM Conference on Computer Science, Vol. 26. No. 10, pp. 418-438, 1993.
  6. H. H. Kim, "Techniques on Multi-Marker for the Implementation of Augmented Reality," Journal of KIISE, Vol.15, No.11, pp.109-116, Nov. 2010. https://doi.org/10.9708/jksci.2010.15.11.109
  7. N. Roussopoulos, S. Kelly, and F. Vincent. "Nearest Neighbor Queries," ACM SIGMOD, 1995.
  8. A. Guttman, "R-tree : A Dynamic Index Structure for Spatial Searching," ACM SIGMOD, 1984.
  9. T. Sellis, N. Roussopouls, C. Faloutsos, "The R+-tree : A Dynamic Index for Multi-Dimensional Objects," VLDB, 1987.
  10. D. S. Kwon, "Protection of Location Privacy for Spatio- Temporal Query Processing Using R-Trees," Journal of Society for E-Business Studies, Vol.15, No.3, pp.85-98, Aug. 2010.
  11. Y. Tao, D. Papadias, and Q. M. Shen. "Continuous Nearest Neighbor Search," Proceeding of VLDB '02, 2002.
  12. S. Han, J. Kim, "A Search Interval Limitation Technique for Improved Search Performance of CNN," journal of Korean Society for Internet Information, Vol.9, No.3, pp.1-8, June 2008.
  13. S. R. Na, K. Y. Hye, "User Location Anonymization Scheme Supporting Continuous Query Processing in Road Network," Journal of KIISE, Vol.17, No.1, pp.41-45, Jan. 2011.
  14. J. Lim, Y. Park, D. Seo, J. Yoo, "An Efficient Continuous Reverse Skyline Query Processing Method in Metric Spaces for Location-based Services," Journal of KIISE: Database, Vol. 37. No.5, pp.250-257, Oct. 2010.
  15. M. S. Kim, H. J. Yoo, J. Chae, W. Choi, "A Vehicles Location Inquiry Technique for Performance Improvement of LBS System," Database, Vol.26, No.3, pp.67-83, Dec. 2010.
  16. J. H. O, J. S. Bae, D. W. Park, Y. H. Sohn, "Implementation of Location Based Service(LBS) using GPS for Various Sizes of Maps," Vol.8, No.4, pp.19-24, Apr. 2010.
  17. K. S. Bok, M.S. Lee, Y. H. Park, J. S. Yoo, "An Effective Location Acquisition Method Based on RFID for Location Based Services," Vol.37, No.1, pp.33-44, Feb. 2010.
  18. Y. Tao, D. Papadias, "Time Parameterized Queries in Spatio-Temporal Databases," ACM SIGMOD, 2002.
  19. S. Ahn, B. Hong, C. Ban, K. Lee, "Design and Implementation of an Index Structure Using Fixed Intervals for Tracing of RFID Tags," ICCSA2006, LNCS3981, pp. 175-185, 2006.
  20. Spatial Data Generator, DaVisual Code1.0. http://isl.cs.unipi.gr.