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A Pattern-Based Framework for Addressing Data Representational Inconsistency

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Databases Theory and Applications (ADC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9877))

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Abstract

Data representational inconsistency, where data has diverse formats or structures, is a crucial data quality problem. Existing fixing approaches either target on a specific domain or require massive information from users. In this work, we propose a user-friendly pattern-based framework for addressing data representational inconsistency. Our framework consists of three modules: pattern design, pattern detection, and pattern unification. We identify several challenges in all the three tasks in order to handle an inconsistent dataset both accurately and efficiently. We propose various techniques to tackle these issues, and our experimental results on real-life datasets demonstrate better performance of our proposals compared with existing methods.

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Acknowledgment

This work was supported by the grant DP140103171 (Declaration, Exploration, Enhancement and Provenance: The DEEP Approach to Data Quality Management Systems) from the Australian Research Council.

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Authors and Affiliations

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

    Bingyu Yi, Wen Hua & Shazia Sadiq

Authors
  1. Bingyu Yi
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  2. Wen Hua
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  3. Shazia Sadiq
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Corresponding author

Correspondence to Bingyu Yi .

Editor information

Editors and Affiliations

  1. Monash University , Clayton, Australia

    Muhammad Aamir Cheema

  2. School of Comp. Science a. Engineer, University of New South Wales School of Comp. Science a. Engineer, Sydney, Australia

    Wenjie Zhang

  3. University of New South Wales , Sydney, New South Wales, Australia

    Lijun Chang

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Yi, B., Hua, W., Sadiq, S. (2016). A Pattern-Based Framework for Addressing Data Representational Inconsistency. In: Cheema, M., Zhang, W., Chang, L. (eds) Databases Theory and Applications. ADC 2016. Lecture Notes in Computer Science(), vol 9877. Springer, Cham. https://doi.org/10.1007/978-3-319-46922-5_31

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  • DOI: https://doi.org/10.1007/978-3-319-46922-5_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46921-8

  • Online ISBN: 978-3-319-46922-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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