MONA: A generic big data management methodology for the high-level and automatic building of FAIR observatories, exploratory studies, and information profiling

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AudienciaPúblico en generales_ES
CoberturaMéxicoes_ES
Fecha de ingreso2026-10-05T16:33:21Z
Fecha de publicación2026-01-01
ResumenBig data science is a popular technology that empowers domain experts to uncover patterns within large datasets. It enables data-driven decision-making (DDDM) users to conduct exploratory analyses and extract actionable insights from vast volumes of information. However, adapting these systems to deliver customized and diverse information products for DDDM users remains a challenge, often requiring substantial manual effort and advanced technical expertise. This paper presents MONA, a big data management methodology to establish a transparent bridge between domain experts and DDDM users, facilitating seamless data exploration and enabling the extraction of meaningful insights. MONA begins with a data profiling model for domain experts to define big data pipelines at a high level. By integrating a profiling-based composition model with mappings of analytical processes, along with a pipeline orchestration framework, it transparently constructs customized big data pipelines that integrate and coordinate the processing, indexing, and storage within a unified data management. This approach enables DDDM experts to design FAIR observatories at a high level by focusing on queries and data visualization needs. MONA was implemented as a data platform and evaluated through the automatic construction of information product observatories, using datasets such as 18 years of Mexico's Pollutant Release Registries fused with Mexican Economic Sectors records, and 23 years of suicide registers in Mexico. The quantitative and qualitative evaluations revealed the suitability of MONA methodology to build FAIR observatories for transforming large and heterogeneous datasets, curated and prepared by domain experts, into vast amounts of interpretable information products, suitable for decision-making on DDDM processes.es_ES
Doihttps://doi.org/10.1016/j.future.2026.108701es_ES
URIhttps://riuat.uat.edu.mx/handle/123456789/5196
Idiomaenes_ES
EditorialELSEVIERes_ES
RelaciónFuture Generation Computer Systemses_ES
URL relacionadohttps://doi.org/10.1016/j.future.2026.108701es_ES
DerechosAcceso restringido / Suscripción (Metadatos de producción científica)es_ES
Licenciahttp://purl.org/coar/access_right/c_16eces_ES
FuenteFuture Generation Computer Systems
Palabra claveData-driven decision-makinges_ES
Palabra claveBig dataes_ES
Palabra claveData orchestrationes_ES
Palabra claveCloud computinges_ES
Palabra claveData analyticses_ES
TítuloMONA: A generic big data management methodology for the high-level and automatic building of FAIR observatories, exploratory studies, and information profilinges_ES
TipoArtículoes_ES
ArbitradoHa sido Arbitradoes_ES
AutorMorin-Garcia, Jose Carlos
AutorBarron-Lugo, J. Armando
AutorReyes-Anastacio, Hugo G.
AutorCastillo-Barrios, Ignacio
AutorGonzalez-Compean, Jose L.
AutorCrespo-Sanchez, Melesio
AutorLopez-Arevalo, Ivan
AutorMorin-Garcia, Jose Carloses_ES
AutorBarron-Lugo, J. Armandoes_ES
AutorReyes-Anastacio, Hugo G.es_ES
AutorCastillo-Barrios, Ignacioes_ES
AutorGonzalez-Compean, Jose L.es_ES
AutorCrespo-Sanchez, Melesioes_ES
AutorLopez-Arevalo, Ivanes_ES
InstituciónUniversidad Autónoma de Tamaulipas
InstituciónUniversidad Autónoma de Tamaulipases_ES
URL relacionadahttps://doi.org/10.1016/j.future.2026.108701
Tipo de artículoIndexado
Tipo de artículoIndexadoes_ES
Volumen185es_ES

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