Indexing Probabilistic Nearest-Neighbor Threshold Queries.
Yinian Qi, Sarvjeet Singh, Rahul Shah, Sunil Prabhakar · 2008
Abstract. Data uncertainty is inherent in many applications, including sensor networks, scientific data management, data integration, locationbased applications, etc. One of common queries for uncertain data is the probabilistic nearest neighbor (PNN) query that returns all uncertain objects with non-zero probabilities to be NN. In this paper we study the PNN query with a probability threshold (PNNT), which returns all objects with the NN probability greater than the threshold. Our PNNT query removes the assumption in all previous papers that the probability of an uncertain object always adds up to 1, i.e., we consider missing probabilities. We propose an augmented R-tree index with additional probabilistic information to facilitate pruning as well as global data structures for maintaining the current pruning status. We present our algorithm for efficiently answering PNNT queries and perform experiments to show that our algorithm significantly reduces the number of objects that need to be further evaluated as NN candidates. 1