Predictive Data Transformation Suggestions in Grafterizer Using Machine Learning

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


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.


Data preprocessing Data transformation Transformation suggestions 



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).


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of OsloOsloNorway
  2. 2.SINTEF ASOsloNorway

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