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Data Origination: Human-Centered Approach for Design, Acquisition, and Utilization of Data

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Proceedings of the 12th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2020) (SoCPaR 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1383))

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Abstract

The development of artificial intelligence and the global emergence of big data have provided access to data from different fields. However, while the reuse and sharing of data resources is vital for cost cutting, the data potentially reflects the design intent of those who design and obtain the data. It is necessary to establish a mechanism to quantify the data quality by sharing information regarding who, for what purpose, and how the target data was acquired. In this study, we discuss the methodology to observe and digitize unobserved events and propose the concept of data origination. Further, we introduce two tools to realize and support data origination: variable quest and TEEDA. Moreover, we explain the limitations of the current approach in achieving the data origination and discuss the approaches to overcome them.

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Acknowledgement

This study was supported by JSPS KAKENHI (JP20H02384), the “Startup Research Program for Post-Corona Society” of Academic Strategy Office, School of Engineering, the University of Tokyo, and the Artificial Intelligence Research Promotion Foundation. We wish to thank Editage for providing English language editing.

Author information

Authors and Affiliations

  1. The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan

    Teruaki Hayashi & Yukio Ohsawa

Authors
  1. Teruaki Hayashi
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  2. Yukio Ohsawa
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Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  2. Systems Innovation, School of Engineering, The University of Tokyo, Bunkyo-ku, Tokyo, Japan

    Yukio Ohsawa

  3. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Niketa Gandhi

  4. Vardhaman College of Engineering, Hyderabad, Telangana, India

    M.A. Jabbar

  5. Faculty of Sciences and Techniques, Hassan 1st University, Settat, Morocco

    Abdelkrim Haqiq

  6. Queen’s University, Belfast, UK

    Seán McLoone

  7. Northumbria University, Newcastle, UK

    Biju Issac

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Hayashi, T., Ohsawa, Y. (2021). Data Origination: Human-Centered Approach for Design, Acquisition, and Utilization of Data. In: Abraham, A., et al. Proceedings of the 12th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2020). SoCPaR 2020. Advances in Intelligent Systems and Computing, vol 1383. Springer, Cham. https://doi.org/10.1007/978-3-030-73689-7_9

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