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S-MRST: a novel framework for indexing uncertain data

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Abstract

This paper studies the problem of probabilistic range query over uncertain data. Although existing solutions could support such query, it still has space for improvement. In this paper, we firstly propose a novel index called S-MRST for indexing uncertain data. For one thing, via using an irregular shape for bounding uncertain data, it has a stronger space pruning ability. For another, by taking the gradient of probability density function into consideration, S-MRST is also powerful in terms of probability pruning ability. More important, S-MRST is a general index which could support multiple types of probabilistic queries. Theoretical analysis and extensive experimental results demonstrate the effectiveness and efficiency of the proposed index.

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Acknowledgment

This work is partially supported by the NSF of China for Outstanding Young Scholars under grant No. 61322208, the NSF of China for Key Program under grant No. 61532021, and the NSF of China under grant Nos. 61272178 and 61572122.

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Authors and Affiliations

  1. College of Information Science and Engineering, Northeastern University, Shenyang, China

    Rui Zhu, Bin Wang, Shiying Luo, Xiaochun Yang & Guoren Wang

Authors
  1. Rui Zhu
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  2. Bin Wang
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  3. Shiying Luo
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  4. Xiaochun Yang
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  5. Guoren Wang
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Correspondence to Bin Wang.

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Zhu, R., Wang, B., Luo, S. et al. S-MRST: a novel framework for indexing uncertain data. World Wide Web 20, 697–727 (2017). https://doi.org/10.1007/s11280-016-0409-x

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  • DOI: https://doi.org/10.1007/s11280-016-0409-x

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