Efficient Rank Based KNN Query Processing Over Uncertain Data

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Efficient Rank Based KNN Query Processing Over Uncertain Data

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by Ying Zhang , Xuemin Lin , Gaoping Zhu , Wenjie Zhang , Qianlu Lin

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BibTeX

@MISC{Zhang_efficientrank,
    author = {Ying Zhang and Xuemin Lin and Gaoping Zhu and Wenjie Zhang and Qianlu Lin},
    title = {Efficient Rank Based KNN Query Processing Over Uncertain Data},
    year = {}
}

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Abstract

Abstract — 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 experiments are conducted on both real and synthetic data to demonstrate the efficiency of our techniques. I.

Keyphrases

knn query processing uncertain data uncertain object efficient rank many application knn query quantitative economics research high io cost unique ranking environmental surveillance uncertain data median rank value invariance important problem cpu efficient algorithm large number comprehensive experiment expected rank theoretical guarantee abstract uncertain data synthetic data

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