ACM Home Page
Please provide us with feedback. Feedback
Shapes based trajectory queries for moving objects
Full text PdfPdf (459 KB)
Source Geographic Information Systems archive
Proceedings of the 13th annual ACM international workshop on Geographic information systems table of contents
Bremen, Germany
SESSION: Moving objects table of contents
Pages: 21 - 30  
Year of Publication: 2005
ISBN:1-59593-146-5
Authors
Bin Lin  University of California, Santa Barbara, CA
Jianwen Su  University of California, Santa Barbara, CA
Sponsors
ACM: Association for Computing Machinery
SIGIR: ACM Special Interest Group on Information Retrieval
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 8,   Downloads (12 Months): 70,   Citation Count: 2
Additional Information:

abstract   references   cited by   index terms   collaborative colleagues  

Tools and Actions: Request Permissions Request Permissions    Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/1097064.1097069
What is a DOI?

ABSTRACT

An interesting issue in moving objects databases is to find similar trajectories of moving objects. Previous work on this topic focuses on movement patterns (trajectories with time dimension) of moving objects, rather than spatial shapes (trajectories without time dimension) of their trajectories. In this paper we propose a simple and effective way to compare spatial shapes of moving object trajectories. We introduce a new distance function based on ``one way distance'' (OWD). Algorithms for evaluating OWD in both continuous (piece wise linear) and discrete (grid representation) cases are developed. An index structure for OWD in grid representation, which guarantees no false dismissals, is also given to improve the efficiency of similarity search. Empirical studies show that OWD out-performs existent methods not only in precision, but also in efficiency. And the results of OWD in continuous case can be approximated by discrete case efficiently.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
T. Brinkhoff. Generating traffic data. Bulletin of the Technical Committee on Data Engineering, IEEE Computer Society, 26(2):19--25, 2003.
 
2
 
3
L. Chen and R. Ng. On the marriage of lp-norms and edit distance. In Proc. VLDB, 2004.
4
 
5
6
7
 
8
E. Keogh, T. Palpanas, V. B. Zordan, D. Gunopulos, and M. Cardle. Indexing large human-motion databases. In Proc. VLDB, 2004.
 
9
S. L. Lee, S. J. Chun, D. H. Kim, J. H. Lee, and C. W. Chung. Similarity search for multidimensional data sequences. In Proc. Int. Conf. on Data Engineering, 2005.
 
10
J. L. Little and Z. Gu. Video retrieval by spatial and temporal sturcture of trajectories. In Proc. of Symp. on Storage and Retrieval for Image and Video Databases, 2001.
11
12
 
13
14
 
15
D. Sankoff and J. B. Kruskal. Time Warps, String Edits, and Macromolecules: The Theory and Practice of Sequence Comparisons. Addison-Wesley, 1983.
16
 
17
 
18