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An optimized data integration model based on reverse cleaning for heterogeneous multi-media data

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Abstract

With the continuous development of information technology, various multi-media data are constantly emerging and presents the characteristics of autonomous and heterogeneous, how to integrate and analysis data more correctly and efficiently has become a challenging problem. Firstly, in order to improve the quality of the integrated data, two real-time threads combined with data adapter are used to monitor and refresh necessary updates from heterogeneous data efficiently. Once the original data has been updated, the real-time data will be loaded into the data center soon. Secondly, a data reverse cleaning method is proposed to improve the data quality. It uses the data source tree that built in the data integration process to find the location of the original data quickly after reverse cleaning. finally, a data accuracy assessment algorithm is designed for data quality assessment, which is based on Bayesian network and the path condition algorithm. Experimental results show that the quality of the integrated data significantly higher than the quality of the original data.

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Acknowledgments

This paper is partly supported by the National Science Foundation of China (Grant No. 61472132, 61472131, and 61300218).

Conflict of interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

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Authors and Affiliations

  1. College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, 410082, China

    Hao Chen, Yueqi Ouyang & Wen Jiang

  2. School of Software, Hunan Vocational College of Science and Technology, Changsha, 410118, China

    Wen Jiang

Authors
  1. Hao Chen
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  2. Yueqi Ouyang
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  3. Wen Jiang
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Corresponding author

Correspondence to Hao Chen.

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Chen, H., Ouyang, Y. & Jiang, W. An optimized data integration model based on reverse cleaning for heterogeneous multi-media data. Multimed Tools Appl 75, 15571–15586 (2016). https://doi.org/10.1007/s11042-015-2683-5

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  • DOI: https://doi.org/10.1007/s11042-015-2683-5

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