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A Formal Taxonomy of Temporal Data Defects

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Data Quality and Trust in Big Data (QUAT 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11235))

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Abstract

Data quality assessment outcomes are essential for analytical processes reliability, especially when they are related to temporal data. Such outcomes depend on efficiency and efficacy of (semi-)automated approaches that are determined by understanding the problem associated with each data defect. Despite the small number of works that describe temporal data defects regarding to accuracy, completeness and consistency, there is a significant heterogeneity of terminology, nomenclature, description depth and number of examined defects. To cover this gap, this work reports a taxonomy that organizes temporal data defects according to a five-step methodology. The proposed taxonomy enhances the descriptions and coverage of defects with regard to the related works, and also supports certain requirements of data quality assessment, including the design of visual analytics solutions to support data quality assessment.

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Author information

Authors and Affiliations

  1. Center of Mathematics, Computing and Cognition, Federal University of ABC, Santo Andre, Brazil

    João Marcelo Borovina Josko

Authors
  1. João Marcelo Borovina Josko
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Correspondence to João Marcelo Borovina Josko .

Editor information

Editors and Affiliations

  1. Zayed University, Dubai, United Arab Emirates

    Hakim Hacid

  2. Macquarie University, Sydney, NSW, Australia

    Quan Z. Sheng

  3. Nara Women’s University, Nara, Japan

    Tetsuya Yoshida

  4. Dalarna University, Borlänge, Sweden

    Azadeh Sarkheyli

  5. Department of Computer Science and Software Engineering, Swinburne University of Technology, Hawthorn, VIC, Australia

    Rui Zhou

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Borovina Josko, J.M. (2019). A Formal Taxonomy of Temporal Data Defects. In: Hacid, H., Sheng, Q., Yoshida, T., Sarkheyli, A., Zhou, R. (eds) Data Quality and Trust in Big Data. QUAT 2018. Lecture Notes in Computer Science(), vol 11235. Springer, Cham. https://doi.org/10.1007/978-3-030-19143-6_7

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  • DOI: https://doi.org/10.1007/978-3-030-19143-6_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-19142-9

  • Online ISBN: 978-3-030-19143-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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