Advertisement

Shape-Based Similarity Query for Trajectory of Mobile Objects

  • Yutaka Yanagisawa
  • Jun-ichi Akahani
  • Tetsuji Satoh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2574)

Abstract

In this paper, we describe an efficient indexing method for a shape-based similarity search of the trajectory of dynamically changing locations of people and mobile objects. In order to manage trajectories in database systems, we define a data model of trajectories as directed lines in a space, and the similarity between trajectories is defined as the Euclidean distance between directed discrete lines. Our proposed similarity query can be used to find interested patterns embedded into the trajectories, for example, the trajectories of mobile cars in a city may include patterns for expecting traffic jams. Furthermore, we propose an efficient indexing method to retrieve similar trajectories for a query by combining a spatial indexing technique (R+-Tree) and a dimension reduction technique, which is called PAA (Piecewise Approximate Aggregate). T he indexing method can efficiently retrieve trajectories whose shape in a space is similar to the shape of a candidate trajectory from the database.

Keywords

Time Series Data Range Query Indexing Method Mobile Object Neighbor Query 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    G. Chen and D. Kotz. Categorizing binary topological relations between regions, lines, and points in geographic databases.Technical Report TR2000-381, A Survey of Context-Aware Mobile Computing Research, Dept. of Computer Science, Dartmouth College, 2000.Google Scholar
  2. [2]
    H. Chon, D. Agrawal, and A.E. Abbadi. Query processing for moving objects with space-time grid storage model.In MDM2002 Conference Proceedings, pages 121–129, 2002.Google Scholar
  3. [3]
    E. Clementini and P.D. Felice. Topological invariants for lines. IEEE Transaction on Knowledge and Data Engineering, 10(1):38–54, 1998.CrossRefGoogle Scholar
  4. [4]
    L.E. Elsgolc. Calculus of Variations.Pergamon Press LTD, 1961.Google Scholar
  5. [5]
    E.G. Hoel and H. Samet. Efficient processing of spatial queries in line segment databases. In O. Gunther and H. J. Schek, editors, SSD’91 Proceedings, volume 525, pages 237–256. Springer-Verlag, 1991.Google Scholar
  6. [6]
    E. Keogh, K. Chakrabarti, S. Mehrotra, and M. Pazzan. Locally adaptive dimensionality reduction for indexing large time series databases.In SIGMOD2001 Conference Proceedings, pages 151–162, 2001.Google Scholar
  7. [7]
    E. Keogh, K. Chakrabarti, M. Pazzani, and S. Mehrotra. Dimensionality reduction for fast similarity search in large time series databases. Knowledge and Information Systems, 3(3):263–286, 2001.zbMATHCrossRefGoogle Scholar
  8. [8]
    G. Kollios, D. Gunopulos, and V.J. Tsotras. On indexing mobile objects. In SIGMOD’99 Conference Proceedings, pages 261–272, 1999.Google Scholar
  9. [9]
    G. Kollios, V.J. Tsotras, D. Gunopulos, A. Delis, and M. Hadjieleftheriou. Indexing animated objects using spatiotemporal access methods. IEEE Transactions on Knowledge and Data Engineering, 13(5):758–777, 2001.CrossRefGoogle Scholar
  10. [10]
    Y.-S. Moon, K.-Y. Whang, and W.-S. Han. General match: A subsequence matching method in time-series databases based on generalized windows.In SIGMOD 2002 Conference Proceedings, pages 382–393, 2002.Google Scholar
  11. [11]
    K. Porkaew, I. Lazaridis, and S. Mehrotra. Querying mobile objects in spatiotemporal databases. In C.S. Jensen, M. Schneider, B. Seeger, and V.J. Tsotras, editors, SSTD 2001, volume 2121 of Lecture Notes in Computer Science, pages 59–78. Springer-Verlag, 2001.Google Scholar
  12. [12]
    N. Priyantha, A. Miu, H. Balakrishnan, and S. Teller. The cricket compass for context-aware mobile applications. In MOBICOM2001 Conference Proceedings, pages 1–14, 2001.Google Scholar
  13. [13]
    T. Sellis, N. Roussopoulos, and C. Faloutsos. The R+-tree: A dynamic index for multidimensional objects. In VLDB’87 Conference Proceedings, pages 3–11, 1987.Google Scholar
  14. [14]
    A.P. Sistla, O. Wolfson, S. Chamberlain, and S. Dao. Modeling and querying moving objects.In ICDE’97 Proceedings, pages 422–432, 1997.Google Scholar
  15. [15]
    M. Vazirgiannis and O. Wolfson. A spatiotemporal model and language for moving objects on road networks. In C. S. Jensen, M. Schneider, B. Seeger, and V. J. Tsotras, editors, SSTD 2001, volume 2121 of Lecture Notes in Computer Science, pages 20–35. Springer-Verlag, 2001.Google Scholar
  16. [16]
    O. Wolfson, B. Xu, S. Chamberlain, and L. Jiang. Moving objects databases: Issues and solutions. In Statistical and Scientific Database Management (SSDM’98) Conference Proceedings, pages 111–122, 1998.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Yutaka Yanagisawa
    • 1
  • Jun-ichi Akahani
    • 1
  • Tetsuji Satoh
    • 1
  1. 1.NTT Communication Science LaboratoriesNTT CorporationUSA

Personalised recommendations