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Research on Data Provenance Model for Multidisciplinary Collaboration

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Book cover Computer Supported Cooperative Work and Social Computing (ChineseCSCW 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 917))

Abstract

Provenance, which can be applied to assure quality, to reinforce reliability, to track fault, and to reproduce process in the end product, refers to record the lifecycle of a piece of data or thing that accounts for its generation, transformation, manipulation, and consumption, together with an explanation of how and why it got to the present place. Recently, due to its extensive applicative domains, the provenance modeling problems have brought to attention of scientific researchers significantly. In this paper, an overview of core components regarding provenance models in existing literature is presented, with a wide width from modelling methods, model comparison, and model practice, to specified issues. In addition, a collaborative model called CollabPG, was built based on the characteristics of multidisciplinary collaboration. Finally, we discussed several issues in relevance with provenance models. This paper mainly presents an overall exploration and analysis, so that potential insights could be provided for both expert and common users to select or design a provenance-based model in arbitrary applications especially multidisciplinary collaboration.

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Acknowledgment

This work was supported by the Joint Fund of National Natural Science Foundation of China and the China Academy of Engineering Physics (NSAF) under Grant No. U1630115, and the National Key Research and Development Program of China under Grant No. 2018YFC0381402.

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Authors and Affiliations

  1. School of Computer Science, Fudan University, Shanghai, China

    Fangyu Yu, Beisi Zhou, Tun Lu & Ning Gu

  2. Shanghai Key Laboratory of Data Science, Fudan University, Shanghai, China

    Fangyu Yu, Beisi Zhou, Tun Lu & Ning Gu

  3. Shanghai Institute of Intelligent Electronics and Systems, Shanghai, China

    Fangyu Yu, Beisi Zhou, Tun Lu & Ning Gu

Authors
  1. Fangyu Yu
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  2. Beisi Zhou
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  3. Tun Lu
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  4. Ning Gu
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Corresponding author

Correspondence to Tun Lu .

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Editors and Affiliations

  1. Shandong University, Jinan, China

    Yuqing Sun

  2. Fudan University, Shanghai, China

    Tun Lu

  3. Guilin University of Technology, Guilin, China

    Xiaolan Xie

  4. University of Shanghai for Science and Technology, Shanghai , China

    Liping Gao

  5. Tongji University, Shanghai, China

    Hongfei Fan

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Yu, F., Zhou, B., Lu, T., Gu, N. (2019). Research on Data Provenance Model for Multidisciplinary Collaboration. In: Sun, Y., Lu, T., Xie, X., Gao, L., Fan, H. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2018. Communications in Computer and Information Science, vol 917. Springer, Singapore. https://doi.org/10.1007/978-981-13-3044-5_3

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  • DOI: https://doi.org/10.1007/978-981-13-3044-5_3

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