A smart data holistic approach for context-aware data analytics (AETHER-UA)

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Título: A smart data holistic approach for context-aware data analytics (AETHER-UA)
Autor/es: Lavalle, Ana | Maté, Alejandro | Trujillo, Juan | Teruel, Miguel A. | Sánchez, Alexander
Grupo/s de investigación o GITE: Lucentia
Centro, Departamento o Servicio: Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos
Palabras clave: Smart Data | Data Integration | Data Bias | Conceptual Modeling | User’s requirements | Modeling of Machine Learning Applications
Fecha de publicación: 15-ene-2024
Editor: CEUR
Cita bibliográfica: ER2023: Companion Proceedings of the 42nd International Conference on Conceptual Modeling: ER Forum, 7th SCME, Project Exhibitions, Posters and Demos, and Doctoral Consortium, November 06-09, 2023, Lisbon, Portugal. CEUR Workshop Proceedings, Vol-3618
Resumen: A smart data holistic approach for context-aware data analytics (AETHER-UA) is one of the four subprojects, developed in the University of Alicante, being part of the whole project AETHER. This project is being developed by four partners: (i) University of Malaga - Coordinator; (ii) University of Alicante, (iii) University of Castilla La-Mancha, and (iv) University of Seville. The project is funded by the Ministry of Science and Innovation. The main goal of this project is to advance towards a knowledge-based framework integrating novel solutions for data, process and business analytics. The research activities for designing and developing Aether will mainly focus on three main challenges: the characterization of the datasets, the improvement and automation of the algorithms, and the generation of mechanisms to enhance model explainability and interpretation of the results. The project is highly related to data processing, integration, analysis and modeling. More concretely, within the AETHER-UA project, several proposals are being developed for the modeling of user’s requirements for Machine Learning applications, the developing of a framework based on Model Driven Development (MDD) for eXplanable Artificial Intelligence and several approaches for the data bias analysis.
URI: http://hdl.handle.net/10045/140788
ISSN: 1613-0073
Idioma: eng
Tipo: info:eu-repo/semantics/conferenceObject
Derechos: © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
Revisión científica: si
Versión del editor: https://ceur-ws.org/Vol-3618/
Aparece en las colecciones:INV - LUCENTIA - Comunicaciones a Congresos, Conferencias, etc.

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