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Title: Materia: A Data Quality Control Embedded Domain Specific Language in Python
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.  more » « less
Award ID(s):
1656958
PAR ID:
10285530
Author(s) / Creator(s):
Date Published:
Journal Name:
BIS 2020: Business Information Systems Workshops
Page Range / eLocation ID:
285-296
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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