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Visual Analysis on Macro Quality Data

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Advances in Artificial Intelligence, Software and Systems Engineering (AHFE 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 787))

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Abstract

Quality inspection and quarantine processing is an important process with extensive business impacts. Data visualization methods failed to fully combine standards and certifications or accreditation. While inspection, quarantine and other quality related measures are hard to analyze due to the multi-factor dimension and therefore it is difficult to reflect the macro quality status. Thus, in order to integrate quality data from quality supervisory inspection and quarantine departments, this paper propose three visualization methods to analyze quality data, such as tree models and histogram visualization, map and histogram visualization, the models implement quality visualization system based on the methods above to realize the comprehensive analysis of macro quality data.

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Acknowledgements

This paper is supported by grants from National Key R&D Program of China (2016YFF0204205) and China National Institute of Standardization (712016Y-4941-2016, 522016Y-4681-2016).

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Authors and Affiliations

  1. China National Institution of Standardization, No. 4 of Zhichun Road, Haidian District, Beijing, China

    Gang Wu, Chao Zhao, Wenxing Ding, Fan Zhang, Jing Zhao & Haitao Wang

  2. AQSIQ Key Laboratory of Human Factors and Ergonomics (CNIS), Beijing, China

    Gang Wu, Chao Zhao, Wenxing Ding, Fan Zhang, Jing Zhao & Haitao Wang

Authors
  1. Gang Wu
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  2. Chao Zhao
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  3. Wenxing Ding
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  4. Fan Zhang
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  5. Jing Zhao
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  6. Haitao Wang
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Corresponding author

Correspondence to Haitao Wang .

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Editors and Affiliations

  1. University of Central Florida, Orlando, Florida, USA

    Tareq Z. Ahram

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Wu, G., Zhao, C., Ding, W., Zhang, F., Zhao, J., Wang, H. (2019). Visual Analysis on Macro Quality Data. In: Ahram, T. (eds) Advances in Artificial Intelligence, Software and Systems Engineering. AHFE 2018. Advances in Intelligent Systems and Computing, vol 787. Springer, Cham. https://doi.org/10.1007/978-3-319-94229-2_10

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