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Index for fast retrieval of uncertain spatial point data
Full text PdfPdf (592 KB)
Source Geographic Information Systems archive
Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems table of contents
Arlington, Virginia, USA
SESSION: Query processing I table of contents
Pages: 195 - 202  
Year of Publication: 2006
ISBN:1-59593-529-0
Authors
Dmitri V. Kalashnikov  University of California, Irvine, Irvine, CA
Yiming Ma  University of California, Irvine, Irvine, CA
Sharad Mehrotra  University of California, Irvine, Irvine, CA
Ramaswamy Hariharan  University of California, Irvine, Irvine, 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): 5,   Downloads (12 Months): 61,   Citation Count: 2
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ABSTRACT

Location information gathered from a variety of sources in the form of sensor data, video streams, human observations, and so on, is often imprecise and uncertain and needs to be represented approximately. To represent such uncertain location information, the use of a probabilistic model that captures the imprecise location as a probability density function (pdf) has been recently proposed. The pdfs can be arbitrarily complex depending on the type of application and the source of imprecision. Hence, efficiently representing, storing and querying pdfs is a very challenging task. While the current state of the art indexing approaches treat the representation and storage of pdfs as a black box, in this paper, we take the challenge of representing and storing any complex pdf in an efficient way. We further develop techniques to index such pdfs to support the efficient processing of location queries. Our extensive experiments demonstrate that our indexing techniques significantly outperform the best existing solutions.


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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R. Cheng, D. V. Kalashnikov, and S. Prabhakar. Evaluation of probabilistic queries over imprecise data in constantly-evolving environments. Information Systems Journal, 2006. to appear.
 
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R. Cheng, S. Prabhakar, and D. V. Kalashnikov. Querying imprecise data in moving object environments. In ICDE, 2003.
 
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R. Cheng, Y. Xia, S. Prabhakar, R. Shah, and J. S. Vitter. Efficient indexing methods for probabilistic threshold queries over uncertain data. In Proc. of VLDB, 2004.
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D. V. Kalashnikov, Y. Ma, S. Mehrotra, R. Hariharan, N. Venkatasubramanian, and N. Ashish. SAT: Spatial Awareness from Textual input. In Proc. of EDBT, 2006.
 
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S. Mehrotra, C. Butts, D. V. Kalashnikov, N. Venkatasubramanian, R. Rao, G. Chockalingam, R. Eguchi, B. Adams, and C. Huyck. Project RESCUE: challenges in responding to the unexpected. SPIE, 5304:179--192, Jan. 2004.
 
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Collaborative Colleagues:
Dmitri V. Kalashnikov: colleagues
Yiming Ma: colleagues
Sharad Mehrotra: colleagues
Ramaswamy Hariharan: colleagues