TY - GEN

T1 - Probabilistic verifiers

T2 - 2008 IEEE 24th International Conference on Data Engineering, ICDE'08

AU - Chengt, Reynold

AU - Chen, Jinchuan

AU - Mokbel, Mohamed

AU - Chow, Chi Yin

PY - 2008/10/1

Y1 - 2008/10/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=52649165058&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=52649165058&partnerID=8YFLogxK

U2 - 10.1109/ICDE.2008.4497506

DO - 10.1109/ICDE.2008.4497506

M3 - Conference contribution

AN - SCOPUS:52649165058

SN - 9781424418374

T3 - Proceedings - International Conference on Data Engineering

SP - 973

EP - 982

BT - Proceedings of the 2008 IEEE 24th International Conference on Data Engineering, ICDE'08

Y2 - 7 April 2008 through 12 April 2008

ER -