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Lavoisier: A DSL for increasing the level of abstraction of data selection and formatting in data mining

Abstract: Input data of a data mining algorithm must conform to a very specific tabular format. Data scientists arrange data into that format by creating long and complex scripts, where different low-level operations are performed, and which can be a time-consuming and error-prone process. To alleviate this situation, we present Lavoisier, a declarative language for data selection and formatting in a data mining context. Using Lavoisier, script size for data preparation can be reduced by 40% on average, and by up to 80% in some cases. Additionally, accidental complexity present in state-of-the-art technologies is considerably mitigated.

 Fuente: Journal of Computer Languages, 2020, 60, 100987

 Editorial: Elsevier

 Fecha de publicación: 01/10/2020

 Nº de páginas: 19

 Tipo de publicación: Artículo de Revista

 DOI: 10.1016/j.cola.2020.100987

 ISSN: 2590-1184,2665-9182

 Proyecto español: TIN2017-86520-C3-3-R

 Url de la publicación: https://doi.org/10.1016/j.cola.2020.100987