On the Reusability of Data Cleaning Workflows

  • Lan Li University of Illinois, Urbana-Champaign https://orcid.org/0000-0003-4499-4126
  • Bertram Ludäscher Professor and Director, Center for Informatics Research in Science and Scholarship; Faculty Affiliate, Department of Computer Science; Faculty Affiliate, National Center for Supercomputing Applications https://orcid.org/0000-0001-9140-936X


The goal of data cleaning is to make data fit for purpose, i.e., to improve data quality, through updates and data transformations, such that downstream analyses can be conducted and lead to trustworthy results. A transparent and reusable data cleaning workflow can save time and effort through automation, and make subsequent data cleaning on new data less errorprone. However, reusability of data cleaning workflows has received little to no attention in the research community. We identify some challenges and opportunities for reusing data cleaning workflows. We present a high-level conceptual model to clarify what we mean by reusability and propose ways to improve reusability along different dimensions. We use the opportunity of presenting at IDCC to invite the community to share their uses cases, experiences, and desiderata for the reuse of data cleaning workflows and recipes in order to foster new collaborations and guide future work.

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