Probabilistic Verifiers: Evaluating Constrained Nearest-Neighbor Queries over Uncertain Data

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 up...

• Type: Conference
• Year: 2008
• Where: ICDE
• Authors: Reynold Cheng, Jinchuan Chen, Mohamed F. Mokbel, Chi-Yin Chow.
• Keywords: Database, Database Systems, Nearest-neighbor Queries, Probabilistic Queries, Uncertain Data