Contextual Reuse of Big Data Systems: A Case Study Assessing Groundwater Recharge Influences

Autores
Buccella, Agustina; Cechich, Alejandra; Garrido, Walter; Montenegro, Ayelén
Año de publicación
2026
Idioma
inglés
Tipo de recurso
artículo
Estado
versión aceptada
Descripción
The process of building data analytics systems, including big data systems, is currently being investigated from various perspectives that generally focus on specific aspects, such as data security or privacy, to the detriment of an engineering perspective on systems development. To address this limitation, our proposal focuses on developing analytics systems through a reuse-based approach, including stages ranging from problem definition to results analysis by identifying variations and building reusable, context-based assets. This study presents the reuse process by constructing two case studies that address the water table level prediction problem in two different contexts: the irrigated period and the non-irrigated period in the same study area. The objective of this study is to demonstrate the influence of context on the performance of widely used predictive models for this problem, including long short-term memory (LSTM), artificial neural networks (ANNs), and support vector machines (SVMs), as well as the potential for reusing the developed analytics system. Additionally, we applied the permutation feature importance (PFI) to determine the contribution of individual variables to the prediction. The results confirm that the same problem hypotheses yield different performance in each case in terms of coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and mean square error (MSE). They also show that the best-performing predictive models differ for some of the hypotheses (ANN in one case and LSTM in another), supporting the assumption that context can influence model selection and performance. Reusing assets allows for more efficient evaluation of these alternatives during development time, resulting in analytics systems that are more closely aligned with reality, while also offering the advantages of software system composition.
Fil: Buccella, Agustina. Universidad Nacional del Comahue. Facultad de Informática; Argentina.
Fil: Cechich, Alejandra. Universidad Nacional del Comahue. Facultad de Informática; Argentina.
Fil: Garrido, Walter. Universidad Nacional del Comahue. Facultad de Informática; Argentina.
Fil: Montenegro, Ayelén. Instituto Nacional de Tecnología Agropecuaria, Alto Valle de Río Negro y Neuquén; Argentina.
Fuente
Applied Sciences. 2026, 16(3)
Materia
Big data systems
Context reusability
Precision agriculture
Prediction grounwater levels
Ciencias de la Computación e Información
Artículos
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
Repositorio Digital Institucional (UNCo)
Institución
Universidad Nacional del Comahue
OAI Identificador
oai:rdi.uncoma.edu.ar:uncomaid/19508
Acceder
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oai_identifier_str oai:rdi.uncoma.edu.ar:uncomaid/19508
network_acronym_str RDIUNCO
repository_id_str 7108
network_name_str Repositorio Digital Institucional (UNCo)
spelling Contextual Reuse of Big Data Systems: A Case Study Assessing Groundwater Recharge InfluencesBuccella, AgustinaCechich, AlejandraGarrido, WalterMontenegro, AyelénBig data systemsContext reusabilityPrecision agriculturePrediction grounwater levelsCiencias de la Computación e InformaciónArtículosThe process of building data analytics systems, including big data systems, is currently being investigated from various perspectives that generally focus on specific aspects, such as data security or privacy, to the detriment of an engineering perspective on systems development. To address this limitation, our proposal focuses on developing analytics systems through a reuse-based approach, including stages ranging from problem definition to results analysis by identifying variations and building reusable, context-based assets. This study presents the reuse process by constructing two case studies that address the water table level prediction problem in two different contexts: the irrigated period and the non-irrigated period in the same study area. The objective of this study is to demonstrate the influence of context on the performance of widely used predictive models for this problem, including long short-term memory (LSTM), artificial neural networks (ANNs), and support vector machines (SVMs), as well as the potential for reusing the developed analytics system. Additionally, we applied the permutation feature importance (PFI) to determine the contribution of individual variables to the prediction. The results confirm that the same problem hypotheses yield different performance in each case in terms of coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and mean square error (MSE). They also show that the best-performing predictive models differ for some of the hypotheses (ANN in one case and LSTM in another), supporting the assumption that context can influence model selection and performance. Reusing assets allows for more efficient evaluation of these alternatives during development time, resulting in analytics systems that are more closely aligned with reality, while also offering the advantages of software system composition.Fil: Buccella, Agustina. Universidad Nacional del Comahue. Facultad de Informática; Argentina.Fil: Cechich, Alejandra. Universidad Nacional del Comahue. Facultad de Informática; Argentina.Fil: Garrido, Walter. Universidad Nacional del Comahue. Facultad de Informática; Argentina.Fil: Montenegro, Ayelén. Instituto Nacional de Tecnología Agropecuaria, Alto Valle de Río Negro y Neuquén; Argentina.MDPI2026info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfpp. 1-30application/pdfhttps://rdi.uncoma.edu.ar/handle/uncomaid/19508Applied Sciences. 2026, 16(3)reponame:Repositorio Digital Institucional (UNCo)instname:Universidad Nacional del Comahueenghttps://doi.org/10.3390/app16031650info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/4.0/2026-08-26T10:58:01Zoai:rdi.uncoma.edu.ar:uncomaid/19508instacron:UNCoInstitucionalhttp://rdi.uncoma.edu.ar/Universidad públicaNo correspondehttp://rdi.uncoma.edu.ar/oaimirtha.mateo@biblioteca.uncoma.edu.ar; adriana.acuna@biblioteca.uncoma.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:71082026-08-26 10:58:01.974Repositorio Digital Institucional (UNCo) - Universidad Nacional del Comahuefalse
dc.title.none.fl_str_mv Contextual Reuse of Big Data Systems: A Case Study Assessing Groundwater Recharge Influences
title Contextual Reuse of Big Data Systems: A Case Study Assessing Groundwater Recharge Influences
spellingShingle Contextual Reuse of Big Data Systems: A Case Study Assessing Groundwater Recharge Influences
Buccella, Agustina
Big data systems
Context reusability
Precision agriculture
Prediction grounwater levels
Ciencias de la Computación e Información
Artículos
title_short Contextual Reuse of Big Data Systems: A Case Study Assessing Groundwater Recharge Influences
title_full Contextual Reuse of Big Data Systems: A Case Study Assessing Groundwater Recharge Influences
title_fullStr Contextual Reuse of Big Data Systems: A Case Study Assessing Groundwater Recharge Influences
title_full_unstemmed Contextual Reuse of Big Data Systems: A Case Study Assessing Groundwater Recharge Influences
title_sort Contextual Reuse of Big Data Systems: A Case Study Assessing Groundwater Recharge Influences
dc.creator.none.fl_str_mv Buccella, Agustina
Cechich, Alejandra
Garrido, Walter
Montenegro, Ayelén
author Buccella, Agustina
author_facet Buccella, Agustina
Cechich, Alejandra
Garrido, Walter
Montenegro, Ayelén
author_role author
author2 Cechich, Alejandra
Garrido, Walter
Montenegro, Ayelén
author2_role author
author
author
dc.subject.none.fl_str_mv Big data systems
Context reusability
Precision agriculture
Prediction grounwater levels
Ciencias de la Computación e Información
Artículos
topic Big data systems
Context reusability
Precision agriculture
Prediction grounwater levels
Ciencias de la Computación e Información
Artículos
dc.description.none.fl_txt_mv The process of building data analytics systems, including big data systems, is currently being investigated from various perspectives that generally focus on specific aspects, such as data security or privacy, to the detriment of an engineering perspective on systems development. To address this limitation, our proposal focuses on developing analytics systems through a reuse-based approach, including stages ranging from problem definition to results analysis by identifying variations and building reusable, context-based assets. This study presents the reuse process by constructing two case studies that address the water table level prediction problem in two different contexts: the irrigated period and the non-irrigated period in the same study area. The objective of this study is to demonstrate the influence of context on the performance of widely used predictive models for this problem, including long short-term memory (LSTM), artificial neural networks (ANNs), and support vector machines (SVMs), as well as the potential for reusing the developed analytics system. Additionally, we applied the permutation feature importance (PFI) to determine the contribution of individual variables to the prediction. The results confirm that the same problem hypotheses yield different performance in each case in terms of coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and mean square error (MSE). They also show that the best-performing predictive models differ for some of the hypotheses (ANN in one case and LSTM in another), supporting the assumption that context can influence model selection and performance. Reusing assets allows for more efficient evaluation of these alternatives during development time, resulting in analytics systems that are more closely aligned with reality, while also offering the advantages of software system composition.
Fil: Buccella, Agustina. Universidad Nacional del Comahue. Facultad de Informática; Argentina.
Fil: Cechich, Alejandra. Universidad Nacional del Comahue. Facultad de Informática; Argentina.
Fil: Garrido, Walter. Universidad Nacional del Comahue. Facultad de Informática; Argentina.
Fil: Montenegro, Ayelén. Instituto Nacional de Tecnología Agropecuaria, Alto Valle de Río Negro y Neuquén; Argentina.
description The process of building data analytics systems, including big data systems, is currently being investigated from various perspectives that generally focus on specific aspects, such as data security or privacy, to the detriment of an engineering perspective on systems development. To address this limitation, our proposal focuses on developing analytics systems through a reuse-based approach, including stages ranging from problem definition to results analysis by identifying variations and building reusable, context-based assets. This study presents the reuse process by constructing two case studies that address the water table level prediction problem in two different contexts: the irrigated period and the non-irrigated period in the same study area. The objective of this study is to demonstrate the influence of context on the performance of widely used predictive models for this problem, including long short-term memory (LSTM), artificial neural networks (ANNs), and support vector machines (SVMs), as well as the potential for reusing the developed analytics system. Additionally, we applied the permutation feature importance (PFI) to determine the contribution of individual variables to the prediction. The results confirm that the same problem hypotheses yield different performance in each case in terms of coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and mean square error (MSE). They also show that the best-performing predictive models differ for some of the hypotheses (ANN in one case and LSTM in another), supporting the assumption that context can influence model selection and performance. Reusing assets allows for more efficient evaluation of these alternatives during development time, resulting in analytics systems that are more closely aligned with reality, while also offering the advantages of software system composition.
publishDate 2026
dc.date.none.fl_str_mv 2026
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://rdi.uncoma.edu.ar/handle/uncomaid/19508
url https://rdi.uncoma.edu.ar/handle/uncomaid/19508
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://doi.org/10.3390/app16031650
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/4.0/
dc.format.none.fl_str_mv application/pdf
pp. 1-30
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv Applied Sciences. 2026, 16(3)
reponame:Repositorio Digital Institucional (UNCo)
instname:Universidad Nacional del Comahue
reponame_str Repositorio Digital Institucional (UNCo)
collection Repositorio Digital Institucional (UNCo)
instname_str Universidad Nacional del Comahue
repository.name.fl_str_mv Repositorio Digital Institucional (UNCo) - Universidad Nacional del Comahue
repository.mail.fl_str_mv mirtha.mateo@biblioteca.uncoma.edu.ar; adriana.acuna@biblioteca.uncoma.edu.ar
_version_ 1874595864902631424
score 13.265058

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