Journal of Korea Spatial Information System Society (한국공간정보시스템학회 논문지)

Korea Spatial Information System Society (대한공간정보학회)
권호 표지

UIL：A Novel Indexing Method for Spatial Objects and Moving Objects

  • Huang, Xuguang (Inha University Dept. of Computer Science and Information Engineering) ;
  • Baek, Sung-Ha (Inha University Dept. of Computer Science and Information Engineering) ;
  • Lee, Dong-Wook (Inha University Dept. of Computer Science and Information Engineering) ;
  • Chung, Weon-Il (Hoseo Universitry Dept. of Information Security Engineering) ;
  • Bae, Hae-Young (Inha University Dept. of Computer Science and Information Engineering)
  • Published : 2009.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

Ubiquitous service based on Spatio-temporal dataspaces requires not only the moving objects data but also the spatial objects. However, existing methods can not handle the moving objects and spatial objects together. To overcome the limitation of existing methods, we propose a new index structure called UIL (Union Indexing Lists) which contains two parts: MOL (Moving Object List) and SOL (Spatial Object List) to index the moving objects and spatial objects together. In addition, it can suppose the flexible queries on these data. We present the results of a series of tests which indicate that the structure perform well.

Keywords

References

  1. X. Xu, J. Han, W. Lu, RT-tree: an improvedR-tree index structure for spatiotemporal databases,Proceedings of the 4th Intl. Symposium onSpatial Data Handling, SDH’90, Zurich,Switzerland,1990, pp. 1040-1049.
  2. Antonin Guttman, R-trees: a dynamic indexstructure for spatial searching, Proceedings ofthe 1984 ACM SIGMOD international conferenceon Management of data, June 18-21, 1984,Boston, Massachusetts.
  3. David B. Lomet, Betty Salzberg, Transaction time database, Temporal databases, 1993, pp. 388-417.
  4. Theodoridis, Y., Vazirgiannis, M., and Sellis, T.:Spatio-Temporal Indexing for Large MultimediaApplications. In Proc. of the 3rd IEEE Int'lConference on Multimedia Computing andSystems, 1996, pp. 441-448.
  5. Mario A. Nascimento, Jefferson R.O. Silva,Towards historical R-trees, Proceeding of the1998 ACM Symposium on Applied Computing,Atlanta, GA, February 1998, pp. 235-240.
  6. Yufei Tao, Dimitris Papadias, MV3R-tree: a spatio-temporal access method for timestamp andinterval queries, Proceedings of 27th InternationalConference on Very Large Data Bases,Roma,Italy,September2001.
  7. Dong Xin, Halevy Alon. Indexing dataspaces. In:Proc. of the 27th Int'l Conf. on Management ofData (SIGMOD 2007). NewYork:ACMPress,2007. 43-54.
  8. Jon Louis Bentley, Donald F. Stanat, E. HollingsWilliams Jr., The complexity of finding fixed-radiusnear neighbors, Information Processing Letters 6 (6), 1977 209-212. https://doi.org/10.1016/0020-0190(77)90070-9
  9. Patel, J.M., Chen, Y., Chakka, V.P., STRIPES: anefficient index for predicated trajectories. In:Proceedings of SIGMOD, 2004, pp. 637-646.
  10. Jensen, C.S., Lin, D., Ooi, B.C., Query and updateefficient B+-tree based indexing of movingobjects. VLDB 2004, pp. 768-779.
  11. Theodoridis, Y., Silva, J. Nascimento, M. On theGeneration of Spatiotemporal Datasets. SSD,1999
  12. Nascimento, M., Silva, J., Thedoridis, Y.Evaluation of Access Structures ofor DiscretelyMoving Points. International Workshop onSpatio-Temporal Database Management, 1999.
  13. Pfoser, D., Jensen, C., Theodoridis, Y. NovelApproaches to the Indexing of Moving ObjectTrajectories. VLDB, 2000.
  14. Miller, Catherine L. TIGER/Line Files TechnicalDocumentation. UA 2000. U.S. Department ofCommerce, Geography Division,U.S. CensusBureau. http://www.census.gov/geo/www/TIGER/TIGERua/ua2ktgr.pdf
  15. J.R. Smith and S.-F. Chang. Quad-TreeSegmentation for Texture-Based Image Query,ACM 2nd Multimedia Conference Proceedings,1994.
  16. J.T. Robinson. The k-D-B-Tree: A SearchStructure for Large Multidimensional DynamicIndexes, Proc. ACM SIGMOD, 1981, pp. 10-18.