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Minimal explanations of missing values by chasing acquisitional data

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Abstract

Acquisitional issues widely exist in GPS and sensor networks. They pertain to where, when, and how often data is physically acquired (sampled) and delivered to some query processing systems. Due to the dynamic environment that the data is acquired with the change of monitoring time, acquisitional data are typically a time-stamped stream where a time-stamped value could either contain noises or be missing. Aiming to improve the quality of data acquisition, we focus on the explanations of missing values in this paper. Several techniques have been developed to provide the explanation on relational data, however, they cannot be directly applied in acquisitional stream data due to its dynamic feature, such as the “change” of acquisitional stream data between two adjacent monitoring time is often constrained by some rules. We show that an explanation could be incorrect or unreasonable if those constraints are not taken into account. We propose one novel chasing technique by considering both spatial and temporal correlations to explain missing values and guarantee a minimal explanation. Experimental results show that our approach can efficiently return high-quality and minimal explanations of missing values.

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Notes

  1. http://pages.swcp.com/stocks/

  2. http://db.csail.mit.edu/labdata/labdata.html

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Acknowledgments

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

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Authors and Affiliations

  1. School of Computer Science and Engineering, Northeastern University, Shenyang, China

    Chuanyu Zong, Xiaochun Yang & Bin Wang

  2. Department of Computer Science and Software Engineering, Swinburne University of Technology, Melbourne, Australia

    Chengfei Liu

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  1. Chuanyu Zong
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  2. Xiaochun Yang
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  3. Bin Wang
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  4. Chengfei Liu
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Correspondence to Xiaochun Yang.

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Zong, C., Yang, X., Wang, B. et al. Minimal explanations of missing values by chasing acquisitional data. World Wide Web 20, 1333–1362 (2017). https://doi.org/10.1007/s11280-017-0438-0

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  • DOI: https://doi.org/10.1007/s11280-017-0438-0

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