Long Beach, CA, USA
Mar. 1, 2010 to Mar. 6, 2010
Xuemin Lin , The University Of New South Wales, Australia
Gaoping Zhu , The University Of New South Wales, Australia
Wenjie Zhang , The University Of New South Wales, Australia
Qianlu Lin , The University Of New South Wales, Australia
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 exact-k, 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 experiments are conducted on both real and synthetic data to demonstrate the efficiency of our techniques.
Xuemin Lin, Gaoping Zhu, Wenjie Zhang, Qianlu Lin, "Efficient rank based KNN query processing over uncertain data", ICDE, 2010, 2013 IEEE 29th International Conference on Data Engineering (ICDE), 2013 IEEE 29th International Conference on Data Engineering (ICDE) 2010, pp. 28-39, doi:10.1109/ICDE.2010.5447874