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Query-Oriented Answer Imputation for Aggregate Queries

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Advances in Databases and Information Systems (ADBIS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11695))

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Abstract

Data imputation is a well-known technique for repairing missing data values but can incur a prohibitive cost when applied to large data sets. Query-driven imputation offers a better alternative as it allows for fixing only the data that is relevant for a query. We adopt a rule-based query rewriting technique for imputing the answers of analytic queries that are missing or suffer from incorrectness due to data incompleteness. We present a novel query rewriting mechanism that is guided by partition patterns which are compact representations of complete and missing data partitions. Our solution strives to infer the largest possible set of missing answers while improving the precision of incorrect ones.

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Notes

  1. 1.

    We omit attribute names when they’re not necessary for understanding.

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Acknowledgement

This work has partially been supported by the EBITA collaborative research project between the Fraunhofer Institute and Sorbonne Université.

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Authors and Affiliations

  1. Sorbonne Université, CNRS, LIP6, Paris, France

    Fatma-Zohra Hannou, Bernd Amann & Mohamed-Amine Baazizi

Authors
  1. Fatma-Zohra Hannou
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  2. Bernd Amann
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  3. Mohamed-Amine Baazizi
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Corresponding author

Correspondence to Bernd Amann .

Editor information

Editors and Affiliations

  1. University of Maribor, Maribor, Slovenia

    Tatjana Welzer

  2. Alpen-Adria Universität Klagenfurt, Klagenfurt, Austria

    Johann Eder

  3. University of Maribor, Maribor, Slovenia

    Vili Podgorelec

  4. University of Maribor, Maribor, Slovenia

    Aida Kamišalić Latifić

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Hannou, FZ., Amann, B., Baazizi, MA. (2019). Query-Oriented Answer Imputation for Aggregate Queries. In: Welzer, T., Eder, J., Podgorelec, V., Kamišalić Latifić, A. (eds) Advances in Databases and Information Systems. ADBIS 2019. Lecture Notes in Computer Science(), vol 11695. Springer, Cham. https://doi.org/10.1007/978-3-030-28730-6_19

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  • DOI: https://doi.org/10.1007/978-3-030-28730-6_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-28729-0

  • Online ISBN: 978-3-030-28730-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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