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On nearest neighbor indexing of nonlinear trajectories
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Source Symposium on Principles of Database Systems archive
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems table of contents
San Diego, California
Pages: 252 - 259  
Year of Publication: 2003
ISBN:1-58113-670-6
Authors
Charu C. Aggarwal  IBM T. J. Watson Research Center, Hawthorne, NY
Dakshi Agrawal  IBM T. J. Watson Research Center, Hawthorne, NY
Sponsors
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGMOD: ACM Special Interest Group on Management of Data
SIGART: ACM Special Interest Group on Artificial Intelligence
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 3,   Downloads (12 Months): 26,   Citation Count: 9
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ABSTRACT

In recent years, the problem of indexing mobile objects has assumed great importance because of its relevance to a wide variety of applications. Most previous results in this area have proposed indexing schemes for objects with linear trajectories in one or two dimensions. In this paper, we present methods for indexing objects with nonlinear trajectories. Specifically, we identify a useful condition called the convex hull property and show that any trajectory satisfying this condition can be indexed by storing a careful representation of these objects in a traditional index structure. Since a wide variety of relevant nonlinear trajectories satisfy this condition, our result significantly expands the class of trajectories for which nearest neighbor indexing schemes can be devised. We also show that even though many non-linear trajectories do not satisfy the convex hull condition, an approximate representation can often be found which satisfies it. We discuss examples of techniques which can be utilized to find representations that satisfy the convex hull property. We present empirical results to demonstrate the effectiveness of our indexing method.


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.

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K. I. Lin, H. V. Jagadish, and C. Faloutsos. The tv-tree: An index structure for high dimensional data. In VLDB Journal, 1992.
 
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D. Pfoser, Y. Theodoridis, and C. Jensen. Indexing trajectories of moving point objects. In VLDB Conference, 2000.
 
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CITED BY  9

Collaborative Colleagues:
Charu C. Aggarwal: colleagues
Dakshi Agrawal: colleagues