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Materia: A Data Quality Control Embedded Domain Specific Language in Python

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Business Information Systems Workshops (BIS 2020)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 394))

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Current solutions for data quality control (QC) in the environmental sciences are locked within propriety platforms or reliant on specialized software. This can pose a problem for data users when attempting to integrate QC into their existing workflows. To address this limitation, we developed an embedded domain specific language (EDSL), Materia, that provides functions, data structures, and a fluent syntax for defining and executing quality control tests on data. Materia enables developers to more easily integrate QC into complex data pipelines and makes QC more accessible for students and citizen scientists. We evaluate Materia via two metrics: productivity and a quantitative performance analysis. Our productivity examples show how Materia can simplify complex descriptions of tests in Pandas and mirror natural language descriptions of common QC tests. We also demonstrate that Materia achieves satisfactory performance with over 200,000 floating-point values processed in under three seconds.

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We thank Michelle Strout for her invaluable input throughout the development of this project and editing provided for this paper. We would also like to thank Kate Isaacs for her input editing this paper and Chase Carthen for his input on the design of this language.

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Correspondence to Connor Scully-Allison .

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Scully-Allison, C. (2020). Materia: A Data Quality Control Embedded Domain Specific Language in Python. In: Abramowicz, W., Klein, G. (eds) Business Information Systems Workshops. BIS 2020. Lecture Notes in Business Information Processing, vol 394. Springer, Cham.

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  • Print ISBN: 978-3-030-61145-3

  • Online ISBN: 978-3-030-61146-0

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