Ponencia
CHAMALEON: Framework to improve Data Wrangling with Complex Data
Autor/es | Valencia Parra, Álvaro
Varela Vaca, Ángel Jesús Gómez López, María Teresa Ceravolo, Paolo |
Departamento | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos |
Fecha de publicación | 2019 |
Fecha de depósito | 2022-07-18 |
Publicado en |
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ISBN/ISSN | ISBN 978-0-9966831-9-7 |
Resumen | Data transformation and schema conciliation are relevant topics in Industry due to the incorporation of data-intensive business processes in organizations. As the amount of data sources increases, the complexity of such ... Data transformation and schema conciliation are relevant topics in Industry due to the incorporation of data-intensive business processes in organizations. As the amount of data sources increases, the complexity of such data increases as well, leading to complex and nested data schemata. Nowadays, novel approaches are being employed in academia and Industry to assist non-expert users in transforming, integrating, and improving the quality of datasets (i.e., data wrangling). However, there is a lack of support for transforming semi-structured complex data. This article makes a state-of-the-art by identifying and analyzing the most relevant solutions that can be found in academia and Industry to transform this type of data. In addition, we propose a Domain-Specific Language (DSL) to support the transformation of complex data as a first approach to enhance data wrangling processes. We also develop a framework to implement the DSL and evaluate it in a real-world case study. |
Agencias financiadoras | Ministerio de Ciencia Y Tecnología (MCYT). España |
Identificador del proyecto | RTI2018-094283-B-C33 |
Cita | Valencia Parra, Á., Varela Vaca, Á.J., Gómez López, M.T. y Ceravolo, P. (2019). CHAMALEON: Framework to improve Data Wrangling with Complex Data. En ICIS 2019 : 4th International Conference on Information Systems Munich, Germany: Association for Information Systems (AIS). |
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