Probabilistic verifiers: Evaluating constrained nearest-neighbor queries over uncertain data

Reynold Chengt, Jinchuan Chen, Mohamed Mokbel, Chi Yin Chow

Research output: Chapter in Book/Report/Conference proceedingConference contribution

117 Citations (Scopus)

Abstract

In applications like location-based services, sensor monitoring and biological databases, the values of the database items are inherently uncertain in nature. An important query for uncertain objects is the Probabilistic Nearest-Neighbor Query (PNN), which computes the probability of each object for being the nearest neighbor of a query point. Evaluating this query is computationally expensive, since it needs to consider the relationship among uncertain objects, and requires the use of numerical integration or Monte-Carlo methods. Sometimes, a query user may not be concerned about the exact probability values. For example, he may only need answers that have sufficiently high confidence. We thus propose the Constrained Nearest-Neighbor Query (C-PNN), which returns the IDs of objects whose probabilities are higher than some threshold, with a given error bound in the answers. The C-PNN can be answered efficiently with probabilistic verifiers. These are methods that derive the lower and upper bounds of answer probabilities, so that an object can be quickly decided on whether it should be included in the answer. We have developed three probabilistic verifiers, which can be used on uncertain data with arbitrary probability density functions. Extensive experiments were performed to examine the effectiveness of these approaches.

Original languageEnglish
Title of host publicationProceedings of the 2008 IEEE 24th International Conference on Data Engineering, ICDE'08
Pages973-982
Number of pages10
DOIs
Publication statusPublished - 1 Oct 2008
Externally publishedYes
Event2008 IEEE 24th International Conference on Data Engineering, ICDE'08 - Cancun, Mexico
Duration: 7 Apr 200812 Apr 2008

Other

Other2008 IEEE 24th International Conference on Data Engineering, ICDE'08
CountryMexico
CityCancun
Period7/4/0812/4/08

Fingerprint

Location based services
Probability density function
Monte Carlo methods
Monitoring
Sensors
Experiments

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems

Cite this

Chengt, R., Chen, J., Mokbel, M., & Chow, C. Y. (2008). Probabilistic verifiers: Evaluating constrained nearest-neighbor queries over uncertain data. In Proceedings of the 2008 IEEE 24th International Conference on Data Engineering, ICDE'08 (pp. 973-982). [4497506] https://doi.org/10.1109/ICDE.2008.4497506

Probabilistic verifiers : Evaluating constrained nearest-neighbor queries over uncertain data. / Chengt, Reynold; Chen, Jinchuan; Mokbel, Mohamed; Chow, Chi Yin.

Proceedings of the 2008 IEEE 24th International Conference on Data Engineering, ICDE'08. 2008. p. 973-982 4497506.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Chengt, R, Chen, J, Mokbel, M & Chow, CY 2008, Probabilistic verifiers: Evaluating constrained nearest-neighbor queries over uncertain data. in Proceedings of the 2008 IEEE 24th International Conference on Data Engineering, ICDE'08., 4497506, pp. 973-982, 2008 IEEE 24th International Conference on Data Engineering, ICDE'08, Cancun, Mexico, 7/4/08. https://doi.org/10.1109/ICDE.2008.4497506
Chengt R, Chen J, Mokbel M, Chow CY. Probabilistic verifiers: Evaluating constrained nearest-neighbor queries over uncertain data. In Proceedings of the 2008 IEEE 24th International Conference on Data Engineering, ICDE'08. 2008. p. 973-982. 4497506 https://doi.org/10.1109/ICDE.2008.4497506
Chengt, Reynold ; Chen, Jinchuan ; Mokbel, Mohamed ; Chow, Chi Yin. / Probabilistic verifiers : Evaluating constrained nearest-neighbor queries over uncertain data. Proceedings of the 2008 IEEE 24th International Conference on Data Engineering, ICDE'08. 2008. pp. 973-982
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