Publicaciones TRS
1.
Buccella, Agustina; Cechich, Alejandra; Garrido, Walter; Montenegro, Ayelén
Contextual Reuse of Big Data Systems: A Case Study Assessing Groundwater Recharge Influences Journal Article
In: Applied Sciences, vol. 16, iss. 3, no. 1650, 2026, ISSN: 2076-3417.
@article{Buccella26,
title = {Contextual Reuse of Big Data Systems: A Case Study Assessing Groundwater Recharge Influences},
author = {Agustina Buccella and Alejandra Cechich and Walter Garrido and Ayelén Montenegro},
url = {https://www.mdpi.com/2076-3417/16/3/1650},
doi = {https://doi.org/10.3390/app16031650},
issn = {2076-3417},
year = {2026},
date = {2026-02-05},
urldate = {2026-02-05},
journal = {Applied Sciences},
volume = {16},
number = {1650},
issue = {3},
abstract = {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.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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.
2.
Acuña, Lara; Keller, Luis; Buccella, Agustina
Un proceso de big data para la recomendación de películas a usuarios en base a su comportamiento Proceedings Article
In: XXXI Congreso Argentino de Ciencias de la Computación, pp. 1190-1190, Universidad Nacional de Río Negro Red de Universidades con Carreras en Informática, Viedma, Argentina, 2025, ISBN: 978-987-8258-99-7.
@inproceedings{Lara25,
title = {Un proceso de big data para la recomendación de películas a usuarios en base a su comportamiento},
author = {Lara Acuña and Luis Keller and Agustina Buccella },
url = {https://sedici.unlp.edu.ar/bitstream/handle/10915/191361/Documento_completo.pdf-PDFA.pdf?sequence=1&isAllowed=y},
isbn = {978-987-8258-99-7},
year = {2025},
date = {2025-10-06},
urldate = {2025-10-06},
booktitle = {XXXI Congreso Argentino de Ciencias de la Computación},
pages = {1190-1190},
publisher = {Red de Universidades con Carreras en Informática},
address = {Viedma, Argentina},
organization = {Universidad Nacional de Río Negro},
abstract = {Muchos sistemas buscan retener a sus consumidores mediante las sugerencias que les ofrecen. Estas deben ser lo suficientemente atractivas y certeras para conseguir la interacción de sus usuarios. Para lograr esto, es necesario realizar un estudio de un gran conjunto de datos.
Este trabajo describe la aplicación de un proceso big data de sistemas recomendadores, en base a datos de usuarios que han puntuado diversas películas.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
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Muchos sistemas buscan retener a sus consumidores mediante las sugerencias que les ofrecen. Estas deben ser lo suficientemente atractivas y certeras para conseguir la interacción de sus usuarios. Para lograr esto, es necesario realizar un estudio de un gran conjunto de datos.
Este trabajo describe la aplicación de un proceso big data de sistemas recomendadores, en base a datos de usuarios que han puntuado diversas películas.
Este trabajo describe la aplicación de un proceso big data de sistemas recomendadores, en base a datos de usuarios que han puntuado diversas películas.
3.
Repetto, Francisco; Rivera, Malena; Buccella, Agustina
Un proceso de Big Data para la predicción de cantidad de casos de Infecciones Respiratorias Agudas en función del Clima Proceedings Article
In: Libro de actas - XXXI Congreso Argentino de Ciencias de la Computación - CACIC 2025, pp. 1185-1189, Universidad Nacional de Río Negro Red de Universidades con Carreras en Informática, Viedma, Argentina, 2025, ISBN: 978-987-8258-99-7.
@inproceedings{Repetto25,
title = {Un proceso de Big Data para la predicción de cantidad de casos de Infecciones Respiratorias Agudas en función del Clima},
author = {Francisco Repetto and Malena Rivera and Agustina Buccella },
url = {https://sedici.unlp.edu.ar/bitstream/handle/10915/191536/Documento_completo.pdf-PDFA.pdf?sequence=1&isAllowed=y},
isbn = {978-987-8258-99-7},
year = {2025},
date = {2025-10-06},
urldate = {2025-10-06},
booktitle = {Libro de actas - XXXI Congreso Argentino de Ciencias de la Computación - CACIC 2025},
pages = {1185-1189},
publisher = {Red de Universidades con Carreras en Informática},
address = {Viedma, Argentina},
organization = {Universidad Nacional de Río Negro},
abstract = {Este trabajo describe la aplicación de un proceso de Big Data orientado a la predicción de la cantidad de casos de infecciones respiratorias agudas en Argentina, empleando datos oficiales de condiciones climáticas y registros de casos correspondientes al período comprendido entre Enero de 2019 y Diciembre de 2024.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
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Este trabajo describe la aplicación de un proceso de Big Data orientado a la predicción de la cantidad de casos de infecciones respiratorias agudas en Argentina, empleando datos oficiales de condiciones climáticas y registros de casos correspondientes al período comprendido entre Enero de 2019 y Diciembre de 2024.
4.
Garrido, Walter; Buccella, Agustina; Cechich, Alejandra; Montenegro, Ayelén
Analysis of Influences on Groundwater Recharge: A Case Study in Contextual Reusability of Big Data Systems Proceedings Article
In: UNSJ – CONICET) Flavio Capraro (Instituto de Automática, Dario Recalde (INTA UNdeC) (Ed.): CAI 2025, 17º Congreso Argentino de Agroinformática 2025 Memorias de las 54 JAIIO , pp. 71-84, SADIO, 2025, ISSN: 2451-7496.
@inproceedings{Garrido25,
title = {Analysis of Influences on Groundwater Recharge: A Case Study in Contextual Reusability of Big Data Systems},
author = {Walter Garrido and Agustina Buccella and Alejandra Cechich and Ayelén Montenegro },
editor = {Flavio Capraro (Instituto de Automática, UNSJ – CONICET), Dario Recalde (INTA UNdeC), María Elena Buemi (UBA/CONICET), Pablo Turjanski (UBA/CONICET)
Diseño: Constanza Ruiz (SADIO)},
url = {https://revistas.unlp.edu.ar/JAIIO/article/view/19669/19838},
issn = {2451-7496},
year = {2025},
date = {2025-09-30},
urldate = {2025-09-30},
booktitle = {CAI 2025, 17º Congreso Argentino de Agroinformática 2025 Memorias de las 54 JAIIO
},
volume = {11},
number = {3},
issue = {3},
pages = {71-84},
publisher = {SADIO},
abstract = {Precision agriculture is a field characterized by the use of technologies to improve various practices associated with crop development. These technologies generate large volumes of data that, when properly managed, can be converted into valuable knowledge for organizations. In this context, this article applies a Big Data Systems (BDS)development methodology, defined in previous work, which allows for the development of application cases in the domain. The objective is to analyze the influence of climatic factors and river flow on the behavior of the groundwater table in the Upper Valley region of Rıo Negro and Neuquen provinces.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Precision agriculture is a field characterized by the use of technologies to improve various practices associated with crop development. These technologies generate large volumes of data that, when properly managed, can be converted into valuable knowledge for organizations. In this context, this article applies a Big Data Systems (BDS)development methodology, defined in previous work, which allows for the development of application cases in the domain. The objective is to analyze the influence of climatic factors and river flow on the behavior of the groundwater table in the Upper Valley region of Rıo Negro and Neuquen provinces.
5.
Buccella, Agustina; Cechich, Alejandra; Saurin, Federico; Montenegro, Ayelén; Rodríguez, Andrea; Muñoz, Angel
A Context-Based Perspective on Frost Analysis in Reuse-Oriented Big Data-System Developments Journal Article
In: Information - Special issue Feature Papers in Information in 2024–2025, vol. 15, iss. 11, no. 661, 2024, ISSN: 2078-2489.
@article{Buccella2024,
title = {A Context-Based Perspective on Frost Analysis in Reuse-Oriented Big Data-System Developments},
author = {Agustina Buccella and Alejandra Cechich and Federico Saurin and Ayelén Montenegro and Andrea Rodríguez and Angel Muñoz},
editor = { Prof. Dr. Willy Susilo},
url = {https://www.mdpi.com/2078-2489/15/11/661},
doi = { 10.3390/info15110661},
issn = {2078-2489},
year = {2024},
date = {2024-10-22},
urldate = {2024-10-22},
journal = {Information - Special issue Feature Papers in Information in 2024–2025},
volume = {15},
number = {661},
issue = {11},
abstract = {The large amount of available data, generated every second via sensors, social networks, organizations, and so on, has generated new lines of research that involve novel methods, techniques, resources, and/or technologies. The development of big data systems (BDSs) can be approached from different perspectives, all of them useful, depending on the objectives pursued. In particular, in this work, we address BDSs in the area of software engineering, contributing to the generation of novel methodologies and techniques for software reuse. In this article, we propose a methodology to develop reusable BDSs by mirroring activities from software product line engineering. This means that the process of building BDSs is approached by analyzing the variety of domain features and modeling them as a family of related assets. The contextual perspective of the proposal, along with its supporting tool, is introduced through a case study in the agrometeorology domain. The characterization of variables for frost analysis exemplifies the importance of identifying variety, as well as the possibility of reusing previous analyses adjusted to the profile of each case. In addition to showing interesting findings from the case, we also exemplify our concept of context variety, which is a core element in modeling reusable BDSs.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The large amount of available data, generated every second via sensors, social networks, organizations, and so on, has generated new lines of research that involve novel methods, techniques, resources, and/or technologies. The development of big data systems (BDSs) can be approached from different perspectives, all of them useful, depending on the objectives pursued. In particular, in this work, we address BDSs in the area of software engineering, contributing to the generation of novel methodologies and techniques for software reuse. In this article, we propose a methodology to develop reusable BDSs by mirroring activities from software product line engineering. This means that the process of building BDSs is approached by analyzing the variety of domain features and modeling them as a family of related assets. The contextual perspective of the proposal, along with its supporting tool, is introduced through a case study in the agrometeorology domain. The characterization of variables for frost analysis exemplifies the importance of identifying variety, as well as the possibility of reusing previous analyses adjusted to the profile of each case. In addition to showing interesting findings from the case, we also exemplify our concept of context variety, which is a core element in modeling reusable BDSs.
6.
Buccella, Agustina; Cechich, Alejandra
Un Modelo para Medir Reusabilidad en el Desarrollo de Sistemas Big Data basados en Contexto Proceedings Article
In: Pesado, Patricia; Thomas, Pablo; Chichizola, Franco (Ed.): Actas del XXX Congreso Argentino de Ciencias de la Computación, pp. 792-801, Red de Universidades con Carreras en Informática, La Plata, 07-11 Octubre, 2024, ISBN: 978-950-34-2428-5.
@inproceedings{Buccella2024b,
title = {Un Modelo para Medir Reusabilidad en el Desarrollo de Sistemas Big Data basados en Contexto},
author = {Agustina Buccella and Alejandra Cechich },
editor = {Patricia Pesado and Pablo Thomas and Franco Chichizola },
url = {https://cacic2024.info.unlp.edu.ar/wp-content/uploads/2024/10/Libro-de-Actas-CACIC-2024-Ebook_.pdf},
isbn = {978-950-34-2428-5},
year = {2024},
date = {2024-10-07},
urldate = {2024-10-07},
booktitle = {Actas del XXX Congreso Argentino de Ciencias de la Computación},
pages = {792-801},
publisher = {Red de Universidades con Carreras en Informática, La Plata, 07-11 Octubre},
abstract = { La reusabilidad en el desarrollo de sistemas big data (SBDs) es aún muy incipiente. En esa área, hemos desarrollado previamente un proceso y una herramienta de soporte (CoVaMaT), que permiten gestionar variedad en SBDs y, de esa manera, incorporar reuso en distintas etapas del ciclo de vida. Sin embargo, el proceso requiere medidas que indiquen la efectividad del reuso alcanzado. En ese sentido, este artículo extiende nuestra propuesta y ejemplifica el uso de medidas de reuso en dos casos de estudio en los dominios de la agricultura de precisión y la agrometeorología.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
La reusabilidad en el desarrollo de sistemas big data (SBDs) es aún muy incipiente. En esa área, hemos desarrollado previamente un proceso y una herramienta de soporte (CoVaMaT), que permiten gestionar variedad en SBDs y, de esa manera, incorporar reuso en distintas etapas del ciclo de vida. Sin embargo, el proceso requiere medidas que indiquen la efectividad del reuso alcanzado. En ese sentido, este artículo extiende nuestra propuesta y ejemplifica el uso de medidas de reuso en dos casos de estudio en los dominios de la agricultura de precisión y la agrometeorología.
7.
Buccella, Agustina; Cechich, Alejandra; Villegas, Carolina; Montenegro, Ayelén; Muñoz, Ángel; Rodríguez, Andrea
A Model of Reusable Assets in AIE Software Systems Journal Article
In: Journal of Computer Science and Technology, vol. 23, iss. 2, no. e13, 2023, ISSN: 1666-6038.
@article{Buccella2023,
title = {A Model of Reusable Assets in AIE Software Systems},
author = {Agustina Buccella and Alejandra Cechich and Carolina Villegas and Ayelén Montenegro and Ángel Muñoz and Andrea Rodríguez},
url = {https://doi.org/10.24215/16666038.23.e13, https://journal.info.unlp.edu.ar/JCST/article/view/2680/1864},
doi = {10.24215/16666038.23.e13},
issn = {1666-6038},
year = {2023},
date = {2023-10-25},
urldate = {2023-10-25},
journal = {Journal of Computer Science and Technology},
volume = {23},
number = {e13},
issue = {2},
abstract = {Nowadays, due to the increasing presence of artificial intelligence in software systems, development teams face the challenge of working together to integrate tasks, resources, and roles in a new field, named AI Engineering. Proposals, in the way of models, highlight the needs of integrating two different perspectives – the software and the decision-making support (big data, machine learning, and so on) systems. But there is something more – both systems must achieve high quality levels for different properties; and this is not a straightforward task. Quality properties, such as reusability, traditionally evaluated and reinforced through modeling in software systems, do not exactly apply similarly in decision-making support systems. In this paper, we propose a model for managing reusable assets in AI engineered systems by linking software product line modeling and variety identification. The proposal is exemplified through a case study in the agriculture domain.},
keywords = {},
pubstate = {published},
tppubtype = {article}
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Nowadays, due to the increasing presence of artificial intelligence in software systems, development teams face the challenge of working together to integrate tasks, resources, and roles in a new field, named AI Engineering. Proposals, in the way of models, highlight the needs of integrating two different perspectives – the software and the decision-making support (big data, machine learning, and so on) systems. But there is something more – both systems must achieve high quality levels for different properties; and this is not a straightforward task. Quality properties, such as reusability, traditionally evaluated and reinforced through modeling in software systems, do not exactly apply similarly in decision-making support systems. In this paper, we propose a model for managing reusable assets in AI engineered systems by linking software product line modeling and variety identification. The proposal is exemplified through a case study in the agriculture domain.
8.
Campetella, Mariano; Cechich, Alejandra; Buccella, Agustina; Montenegro, Ayelén; Muñoz, Ángel; Rodríguez, Andrea
Identificación top-down de variedad de contexto: un caso de estudio en fluctuaciones de la napa freática Proceedings Article
In: Fernández, Juan Manuel (Ed.): Libro de actas - XXIX Congreso Argentino de Ciencias de la Computación, 9-12 Octubre, Luján, Buenos Aires, pp. 308-317, Red de Universidades con Carreras en Informática, 2023, ISBN: 978-987-9285-51-0.
@inproceedings{Campetella2023,
title = {Identificación top-down de variedad de contexto: un caso de estudio en fluctuaciones de la napa freática},
author = {Mariano Campetella and Alejandra Cechich and Agustina Buccella and Ayelén Montenegro and Ángel Muñoz and Andrea Rodríguez },
editor = { Fernández, Juan Manuel},
url = {https://sedici.unlp.edu.ar/handle/10915/164924},
isbn = {978-987-9285-51-0},
year = {2023},
date = {2023-10-09},
urldate = {2023-10-09},
booktitle = {Libro de actas - XXIX Congreso Argentino de Ciencias de la Computación, 9-12 Octubre, Luján, Buenos Aires},
pages = {308-317},
publisher = {Red de Universidades con Carreras en Informática},
abstract = {Considerando la cantidad y diversidad en los datos que hoy día se relevan para futuros análisis, su combinación y uso se torna un elemento complejo a modelar. Es por esto que el agregado de semántica, a través de modelos conceptuales, es una tendencia actual en las arquitecturas software para Sistemas Big Data. En ese sentido, en este artículo presentamos una caracterización de contexto mediante la identificación top-down de variedad en sistemas predictivos sobre fluctuaciones de cuerpos de aguas subterráneos. Esa caracterización favorecería la identificación de situaciones recurrentes, incluyendo la posibilidad de reusabilidad durante el análisis. La propuesta se ejemplifica mediante dos casos comparativos en zonas geográficas diferentes y distantes.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Considerando la cantidad y diversidad en los datos que hoy día se relevan para futuros análisis, su combinación y uso se torna un elemento complejo a modelar. Es por esto que el agregado de semántica, a través de modelos conceptuales, es una tendencia actual en las arquitecturas software para Sistemas Big Data. En ese sentido, en este artículo presentamos una caracterización de contexto mediante la identificación top-down de variedad en sistemas predictivos sobre fluctuaciones de cuerpos de aguas subterráneos. Esa caracterización favorecería la identificación de situaciones recurrentes, incluyendo la posibilidad de reusabilidad durante el análisis. La propuesta se ejemplifica mediante dos casos comparativos en zonas geográficas diferentes y distantes.
9.
Osycka, Líam; Cechich, Alejandra; Buccella, Agustina; Montenegro, Ayelén; Muñoz, Ángel
CoVaMaT: Functionality for Variety Reuse Through a Supporting Tool Proceedings Article
In: Naiouf, Marcelo R.; Rucci, Enzo; Chichizola, Franco; Giusti, Laura C. De (Ed.): Cloud Computing, Big Data & Emerging Topics - 11th Conference, JCC-BD & ET., pp. 57-74, Springer, 2023, ISBN: 978-3-031-40941-7.
@inproceedings{Osycka2023,
title = {CoVaMaT: Functionality for Variety Reuse Through a Supporting Tool},
author = {Líam Osycka and Alejandra Cechich and Agustina Buccella and Ayelén Montenegro and Ángel Muñoz},
editor = {Marcelo R. Naiouf and Enzo Rucci and Franco Chichizola and Laura C. De Giusti},
url = {https://doi.org/10.1007/978-3-031-40942-4_5},
doi = {10.1007/978-3-031-40942-4_5},
isbn = {978-3-031-40941-7},
year = {2023},
date = {2023-06-29},
urldate = {2023-06-29},
booktitle = {Cloud Computing, Big Data & Emerging Topics - 11th Conference, JCC-BD & ET.},
volume = {1828},
pages = {57-74},
publisher = {Springer},
series = {Communications in Computer and Information Science},
abstract = {Developing reusable Big Data Systems (BDSs) implies dealing with modeling variety as reusable assets. Conceptually speaking, these assets might be similar to reusable software artifacts built under software product line (SPL) engineering; however, similar does not imply they are the same. Variety identification in BDSs is more related to collecting and preparing data, and of course, analytics; meanwhile SPLs model reusable pieces of software. Although in the end all it is about software, its nature differs as treatment for its reuse does. In this paper, we introduce our proposal for modeling reusable variety by describing the way it is processed by our supporting tool CoVaMaT (Context-Based Variety Management Tool). We exemplified its functionality through two case studies in the precision agriculture domain.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Developing reusable Big Data Systems (BDSs) implies dealing with modeling variety as reusable assets. Conceptually speaking, these assets might be similar to reusable software artifacts built under software product line (SPL) engineering; however, similar does not imply they are the same. Variety identification in BDSs is more related to collecting and preparing data, and of course, analytics; meanwhile SPLs model reusable pieces of software. Although in the end all it is about software, its nature differs as treatment for its reuse does. In this paper, we introduce our proposal for modeling reusable variety by describing the way it is processed by our supporting tool CoVaMaT (Context-Based Variety Management Tool). We exemplified its functionality through two case studies in the precision agriculture domain.
10.
Buccella, Agustina; Cechich, Alejandra; Osycka, Líam; Luzuriaga, Juan; Villegas, Carolina; Cruz, Marcos; Corgatelli, Franco; Martínez, Rodolfo; Mazalu, Rafaela
CoVaMaT: Modelo conceptual de una herramienta para el soporte a la gestión de variedad en Sistemas Big Data Proceedings Article
In: Sarobe, Mónica; Cicerchia, Lucas Benjamín; Ramón, Hugo Dionisio; Esnaola, Leonardo; Tessore, Juan Pablo; Russo, Claudia Cecilia (Ed.): Libro de Actas WICC 2023- XXV Workshop de Investigadores en Ciencias de la Computación, pp. 439-443, Red de Universidades con Carreras en Informática, 2023, ISBN: 978-987-3724-66-4.
@inproceedings{Buccella2023b,
title = {CoVaMaT: Modelo conceptual de una herramienta para el soporte a la gestión de variedad en Sistemas Big Data},
author = {Agustina Buccella and Alejandra Cechich and Líam Osycka and Juan Luzuriaga and Carolina Villegas and Marcos Cruz and Franco Corgatelli and Rodolfo Martínez and Rafaela Mazalu},
editor = {Mónica Sarobe and Lucas Benjamín Cicerchia and Hugo Dionisio Ramón and Leonardo Esnaola and Juan Pablo Tessore and Claudia Cecilia Russo},
url = {http://sedici.unlp.edu.ar/handle/10915/162004},
isbn = {978-987-3724-66-4},
year = {2023},
date = {2023-04-13},
urldate = {2023-07-15},
booktitle = {Libro de Actas WICC 2023- XXV Workshop de Investigadores en Ciencias de la Computación},
pages = {439-443},
publisher = {Red de Universidades con Carreras en Informática},
abstract = {Un cambio importante con respecto a depósitos de datos tradicionales, es que en los Sistemas Big Data (SBDs) la naturaleza no estructurada de algunos datos puede provenir de diversas fuentes, entre ellas sensores, redes sociales, entorno y la misma empresa. La diversidad de esos datos puede analizarse abordando distintas características. Precisamente, la propiedad de los SBDs con respecto a diversidad de los datos se denomina emphVariedad.
Nuestro proyecto propone modelar variedad mediante casos documentados a través de las variaciones que diferentes variables pueden tomar en un contexto. Sin embargo, para la aplicación de la propuesta, es indispensable una herramienta de soporte que construya incrementalmente repositorios de variaciones a ser reusadas.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Un cambio importante con respecto a depósitos de datos tradicionales, es que en los Sistemas Big Data (SBDs) la naturaleza no estructurada de algunos datos puede provenir de diversas fuentes, entre ellas sensores, redes sociales, entorno y la misma empresa. La diversidad de esos datos puede analizarse abordando distintas características. Precisamente, la propiedad de los SBDs con respecto a diversidad de los datos se denomina emphVariedad.
Nuestro proyecto propone modelar variedad mediante casos documentados a través de las variaciones que diferentes variables pueden tomar en un contexto. Sin embargo, para la aplicación de la propuesta, es indispensable una herramienta de soporte que construya incrementalmente repositorios de variaciones a ser reusadas.
Nuestro proyecto propone modelar variedad mediante casos documentados a través de las variaciones que diferentes variables pueden tomar en un contexto. Sin embargo, para la aplicación de la propuesta, es indispensable una herramienta de soporte que construya incrementalmente repositorios de variaciones a ser reusadas.