KIPS Transactions on Software and Data Engineering (정보처리학회논문지:소프트웨어 및 데이터공학)

Korea Information Processing Society (한국정보처리학회)
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A Technique for Generating Semantic Trajectories by Using GPS Positions and POI Information

GPS 이동 궤적과 관심지점 정보를 이용한 시맨틱 궤적 생성 기법

  • 장유희 (연세대학교 전산학과) ;
  • 이주원 (연세대학교 전산학과) ;
  • 임효상 (연세대학교 컴퓨터정보통신공학부)
  • Received : 2015.07.16
  • Accepted : 2015.08.26
  • Published : 2015.10.31
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Abstract

Recently, semantic trajectories which combine GPS positions and POIs(Point of Interests) become more popular in order to expand location based services. To construct semantic trajectories, the existing algorithms exploit the extent information of POIs described as polygons and find overlapping regions between GPS positions and the extents. However, the algorithms are not applicable in the condition where the extent information is not provided such as in Google Map, Naver Map, OpenStreetMap and most of the open geographic information systems. In this paper, we provide a novel algorithm to construct semantic trajectories only with GPS positions and POI points but without POI extents.

최근 위치기반서비스의 확장을 위해 GPS 위치정보에 관심지점(POI: Point of Interest) 정보를 결합한 시맨틱 궤적(Semantic Trajectory)이 주목받고 있다. 기존 연구의 경우 GPS 궤적과 POI의 면적정보(polygon)가 겹치는 경우를 찾아내어 시맨틱 궤적을 생성하였다. 하지만 구글 지도, 네이버 지도, OpenStreetMap 등과 같은 공개된 지리 정보 시스템에서는 POI의 면적정보를 제공하지 않기 때문에 기존 방법으로는 시맨틱궤적을 생성하지 못하는 문제가 있다. 본 논문에서는 POI의 면적정보가 없는 제한적인 상황에서도 GPS 위치정보와 POI의 좌표값(points)만을 이용하여 시맨틱 궤적을 생성할 수 있는 기법을 제안한다.

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References

  1. Giannotti, Fosca, et al., "Trajectory pattern mining," Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, 2007
  2. Li, Quannan, et al., "Mining user similarity based on location history," Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems, ACM, 2008.
  3. Parent, Christine, et al., "Semantic trajectories modeling and analysis," ACM Computing Surveys (CSUR), Vol.45, No.4, (2013): 42.
  4. Ye, Yang, et al., "Mining individual life pattern based on location history," Mobile Data Management: Systems, Services and Middleware, 2009. MDM'09. Tenth International Conference on, IEEE, 2009.
  5. Alvares, Luis Otavio, et al., "Dynamic modeling of trajectory patterns using data mining and reverse engineering," Tutorials, posters, panels and industrial contributions at the 26th international conference on Conceptual modeling-Volume 83, Australian Computer Society, Inc., 2007.
  6. Xie, Kexin, Ke Deng, and Xiaofang Zhou, "From trajectories to activities: a spatio-temporal join approach," Proceedings of the 2009 International Workshop on Location Based Social Networks. ACM, 2009.
  7. Renso, Chiara, et al., "How you move reveals who you are: understanding human behavior by analyzing trajectory data." Knowledge and Information Systems, Vol.37, No.2, pp.331-362. 2013. https://doi.org/10.1007/s10115-012-0511-z
  8. Ying, Josh Jia-Ching, et al., "Semantic trajectory mining for location prediction," Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM, 2011.
  9. Alvares, Luis Otavio, et al., "A model for enriching trajectories with semantic geographical information," Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems, ACM, 2007.
  10. 김회평, "걷는 속도," 문화일보 신문기사 [Internet], http://www.munhwa.com/news/view.html?no=2007050401033037076002. 5, 2007.