Efficient rank based KNN query processing over uncertain data

Ying Zhang, Xuemin Lin, Gaoping Zhu, Wenjie Zhang, Qianlu Lin
2010 2010 IEEE 26th International Conference on Data Engineering (ICDE 2010)  
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
more » ... e 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 experiments are conducted on both real and synthetic data to demonstrate the efficiency of our techniques.
doi:10.1109/icde.2010.5447874 dblp:conf/icde/ZhangLZZL10 fatcat:dz7o4ct7fjavhebrsheorzq3wa