RT Journal Article
T1 BIGOWL4DQ: ontology-driven approach for Big Data quality meta-modelling, selection and reasoning
A1 Barba-González, Cristóbal
A1 Caballero, Ismael
A1 Varela-Vaca, Ángel Jesús
A1 Cruz-Lemus, José A.
A1 Gómez López, María Teresa
A1 Navas-Delgado, Ismael
K1 Datos masivos
K1 Ontología
K1 Ingeniería del software
AB Context:Data quality should be at the core of many Artificial Intelligence initiatives from the very first moment in which data is required for a successful analysis. Measurement and evaluation of the level of quality are crucial to determining whether data can be used for the tasks at hand. Conscientious of this importance, industry and academia have proposed several data quality measurements and assessment frameworks over the last two decades. Unfortunately, there is no common and shared vocabulary for data quality terms. Thus, it is difficult and time-consuming to integrate data quality analysis within a (Big) Data workflow for performing Artificial Intelligence tasks. One of the main reasons is that, except for a reduced number of proposals, the presented vocabularies are neither machine-readable nor processable, needing human processing to be incorporated.Objective:This paper proposes a unified data quality measurement and assessment information model. This model can be used in different environments and contexts to describe data quality measurement and evaluation concerns.Method:The model has been developed as an ontology to make it interoperable and machine-readable. For better interoperability and applicability, this ontology, BIGOWL4DQ, has been developed as an extension of a previously developed ontology for describing knowledge management in Big Data analytics.Conclusions:This extended ontology provides a data quality measurement and assessment framework required when designing Artificial Intelligence workflows and integrated reasoning capacities. Thus, BIGOWL4DQ can be used to describe Big Data analysis and assess the data quality before the analysis.Result:Our proposal has been validated with two use cases. First, the semantic proposal has been assessed using an academic use case. And second, a real-world case study within an Artificial Intelligence workflow has been conducted to endorse our work.
PB Elsevier
YR 2023
FD 2023-11-27
LK https://hdl.handle.net/10630/28974
UL https://hdl.handle.net/10630/28974
LA eng
NO Cristóbal Barba-González, Ismael Caballero, Ángel Jesús Varela-Vaca, José A. Cruz-Lemus, María Teresa Gómez-López, Ismael Navas-Delgado, BIGOWL4DQ: Ontology-driven approach for Big Data quality meta-modelling, selection and reasoning, Information and Software Technology, Volume 167, 2024, 107378, ISSN 0950-5849, https://doi.org/10.1016/j.infsof.2023.107378. (https://www.sciencedirect.com/science/article/pii/S0950584923002331)
NO Funding for open access charge: Universidad de Málaga/CBUA. This publication is part of the R+D+d projects PID2020-112540RB-C41 (AETHER-UMA), PID2020-112540RB-C42 (AETHER-UCLM) and PID2020-112540RB-C44 (AETHER-US): A smart data holistic approach for context-aware data analytics, all of which are funded by MCIN/AEI/ 10.13039/501100011033/. Also, it has been partially funded by the R&D projects METAMORFOSIS, Spain (US-1381375) from Junta de Andalucía, and ADAGIO, Alarcos’ DAta Governance framework and systems generation, Spain (SBPLY/21/180501/000061), funded by the Consejería de Educación, Cultura Deportes of the Junta de Comunidades de Castilla-La Mancha (Spain).
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026