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Efficient histogram-based range query estimation for dirty data

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Abstract

In recent years, data quality issues have attracted wide attentions. Data quality problems are mainly caused by dirty data. Currently, many methods for dirty data management have been proposed, and one of them is entity-based relational database in which one tuple represents an entity. The traditional query optimizations are not suitable for the new entity-based model. Then new query optimizations need to be developed. In this paper, we propose a new query selectivity estimation strategy based on histogram, and focus on solving the overestimation which traditional methods lead to. We prove our approaches are unbiased. The experimental results on both real and synthetic data sets show that our approaches can give good estimates with low error.

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Acknowledgements

This paper was partially supported by the National Natural Science Foundation of China (Grant Nos. U1509216 and 61472099), National Sci-Tech Support Plan (2015BAH10F01), the Scientific Research Foundation for the Returned Overseas Chinese Scholars of Heilongjiang Province (LC2016026), and MOE–Microsoft Key Laboratory of Natural Language Processing and Speech, Harbin Institute of Technology, China.

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Authors and Affiliations

  1. Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China

    Yan Zhang, Hongzhi Wang, Long Yang & Jianzhong Li

Authors
  1. Yan Zhang
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  2. Hongzhi Wang
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  3. Long Yang
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  4. Jianzhong Li
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Corresponding author

Correspondence to Hongzhi Wang.

Additional information

Yan Zhang is an associate professor and master supervisor at Harbin Institute of Technology, China. His research area includes data quality and bioinformatics.

Hongzhi Wang is a professor and doctoral supervisor at Harbin Institute of Technology, China. His research area is big data management, including data quality, XML data management and graph management. He is a recipient of the outstanding dissertation award of CCF, Microsoft Fellow and IBM PhD Fellowship.

Long Yang is a master student at Harbin Institute of Technology, China. His research area is data quality management.

Jianzhong Li is a professor and doctoral supervisor at Harbin Institute of Technology, China. He is a senior member of CCF. His research interests include database, parallel computing and wireless sensor networks, etc.

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Zhang, Y., Wang, H., Yang, L. et al. Efficient histogram-based range query estimation for dirty data. Front. Comput. Sci. 12, 984–999 (2018). https://doi.org/10.1007/s11704-016-5551-1

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