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International Conference on Conceptual Modeling

ER 2022: Conceptual Modeling pp 310–324Cite as

A Behavioural Analysis of Metadata Use in Evaluating the Quality of Repurposed Data

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13607))

Abstract

Existing approaches for evaluating data quality were established for settings where user requirements regarding data use could be explicitly gathered. Currently, however, users are often faced with new, unfamiliar, and repurposed datasets where they have not been involved in the data collection and creation processes. Furthermore, there is evidence that despite various standardisation initiatives, supporting information or metadata for such datasets is provided in a variety of ways or even lacking altogether. Yet, users need to evaluate the quality of such data to determine if it is suitable for their intended purposes. In this regard, there is limited understanding of the role of metadata in evaluating the quality of repurposed datasets. Thus, in this paper, we aim to investigate how users engage with metadata during data repurposing tasks. In particular, we gather multi-modal user behaviour data through a lab experiment, using eye-tracking techniques and cued-retrospective think-aloud analysis to explore when, how and why users use metadata in such tasks. The results of our study shed light on the critical role metadata plays in evaluating repurposed data, highlight the existence of relationships between data quality error type and metadata, and identify a number of metadata usage patterns relative to the task. This bears implications for the design of systems or tools related to data quality discovery and evaluation.

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Notes

  1. 1.

    For more specifications of the eye tracker, please visit: https://www.tobiipro.com/product-listing/tobii-pro-tx300/, last accessed 2022/04/19.

  2. 2.

    A fixation is the time span when the eye remains still at a specific position of the stimulus.

  3. 3.

    https://www.ataccama.com/platform/data-quality, last accessed 2022/04/19.

  4. 4.

    https://data.gov/, last accessed 2022/04/19.

  5. 5.

    https://rapidminer.com, last accessed 2022/04/19.

  6. 6.

    https://www.dropbox.com/sh/5417f2qdcwelngw/AABsU9JdbMgrA6pKDQF1xSM5a?dl=0.

  7. 7.

    We extracted the subjective insights from the transcripts using Nvivo 12, following a methodology to develop recurring aspects and group them into categories [32].

  8. 8.

    1). How do you define missing data in the data quality context? 2). Can you please give one example of missing data? 3). Can you please mention some data quality dimensions/attributes? 4). Can you please explain what is meant by data quality dimensions/attributes?

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Acknowledgements

This study was supported by the Australian Research Council through ARC Discovery Grant DP190102141.

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Authors and Affiliations

  1. School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD, Australia

    Hui Zhou, Lei Han, Gianluca Dermatini & Shazia Sadiq

  2. Business School, The University of Queensland, Brisbane, QLD, Australia

    Marta Indulska

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  1. Hui Zhou
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  2. Lei Han
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  3. Gianluca Dermatini
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  4. Marta Indulska
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  5. Shazia Sadiq
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Corresponding author

Correspondence to Hui Zhou .

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Editors and Affiliations

  1. University of Geneva, Carouge, Switzerland

    Jolita Ralyté

  2. The University of Texas at Arlington, Arlington, TX, USA

    Sharma Chakravarthy

  3. IIIT Delhi, New Delhi, India

    Mukesh Mohania

  4. University of Skövde, Skövde, Sweden

    Manfred A. Jeusfeld

  5. International Institute of Information Technology Gachibowli, Hyderabad, India

    Kamalakar Karlapalem

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Zhou, H., Han, L., Dermatini, G., Indulska, M., Sadiq, S. (2022). A Behavioural Analysis of Metadata Use in Evaluating the Quality of Repurposed Data. In: Ralyté, J., Chakravarthy, S., Mohania, M., Jeusfeld, M.A., Karlapalem, K. (eds) Conceptual Modeling. ER 2022. Lecture Notes in Computer Science, vol 13607. Springer, Cham. https://doi.org/10.1007/978-3-031-17995-2_22

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  • DOI: https://doi.org/10.1007/978-3-031-17995-2_22

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