Efficient Rank Based KNN Query Processing Over Uncertain Data

Uncertain data are inherent in many applications such as environmental surveillance and quantitative economics research. As an important problem in many applications, KNN query has been extensively investigated in the literature. In this paper, we study the problem of processing rank based KNN query against uncertain data. Besides applying the expected rank semantic to compute KNN, we also introduce the median rank which is less sensitive to the outliers. We show both ranking methods satisfy nice top-k properties such as exactk, containment, unique ranking, value invariance, stability and fairfulness. For given query q, IO and CPU efficient algorithms are proposed in the paper to compute KNN based on expected (median) ranks of the uncertain objects. To tackle the correlations of the uncertain objects and high IO cost caused by large number of instances of the uncertain objects, randomized algorithms are proposed to approximately compute KNN with theoretical guarantees. Comprehensive ex...

• Type: Conference
• Year: 2010
• Where: ICDE
• Authors: Ying Zhang, Xuemin Lin, Gaoping ZHU, Wenjie Zhang, Qianlu Lin
• Keywords: Database, ICDE 2010, KNN Query, Rank Based Knn, Uncertain Objects