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Predictive Data Transformation Suggestions in Grafterizer Using Machine Learning

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Metadata and Semantic Research (MTSR 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1057))

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Abstract

Data preprocessing is a crucial step in data analysis. A substantial amount of time is spent on data transformation tasks such as data formatting, modification, extraction, and enrichment, typically making it more convenient for users to work with systems that can recommend most relevant transformations for a given dataset. In this paper, we propose an approach for generating relevant data transformation suggestions for tabular data preprocessing using machine learning (specifically, the Random Forest algorithm). The approach is implemented for Grafterizer, a Web-based framework for tabular data cleaning and transformation, and evaluated through a usability study.

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Notes

  1. 1.

    https://pandas.pydata.org.

  2. 2.

    https://scikit-learn.org.

  3. 3.

    https://www.r-project.org.

  4. 4.

    https://www.trifacta.com.

  5. 5.

    https://www.tableau.com.

  6. 6.

    https://www.talend.com/products/data-preparation.

  7. 7.

    http://www.holoclean.io.

  8. 8.

    http://openrefine.org.

  9. 9.

    https://angular.io.

  10. 10.

    https://www.typescriptlang.org.

  11. 11.

    https://www.npmjs.com/package/random-forest-classifier.

  12. 12.

    https://en.wikipedia.org/wiki/NaN(Not-A-Number).

  13. 13.

    https://opencorporates.com.

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Acknowledgements

The work in this paper was partly funded by the EC H2020 projects euBusinessGraph (Grant nr. 732003), EW-Shopp (Grant nr. 732590), and TheyBuyForYou (Grant nr. 780247).

Author information

Authors and Affiliations

  1. University of Oslo, Oslo, Norway

    Saliha Sajid & Dumitru Roman

  2. SINTEF AS, Oslo, Norway

    Saliha Sajid, Bjørn Marius von Zernichow, Ahmet Soylu & Dumitru Roman

Authors
  1. Saliha Sajid
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  2. Bjørn Marius von Zernichow
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  3. Ahmet Soylu
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  4. Dumitru Roman
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Corresponding author

Correspondence to Ahmet Soylu .

Editor information

Editors and Affiliations

  1. International Hellenic University, Thessaloniki, Greece

    Emmanouel Garoufallou

  2. Guglielmo Marconi University, Rome, Italy

    Francesca Fallucchi

  3. Georg Eckert Institute – Leibniz Institute for International Textbook Research, Braunschweig, Germany

    Ernesto William De Luca

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Sajid, S., von Zernichow, B.M., Soylu, A., Roman, D. (2019). Predictive Data Transformation Suggestions in Grafterizer Using Machine Learning. In: Garoufallou, E., Fallucchi, F., William De Luca, E. (eds) Metadata and Semantic Research. MTSR 2019. Communications in Computer and Information Science, vol 1057. Springer, Cham. https://doi.org/10.1007/978-3-030-36599-8_12

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  • DOI: https://doi.org/10.1007/978-3-030-36599-8_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36598-1

  • Online ISBN: 978-3-030-36599-8

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