Correo Electrónico
DNINFOA - SIA
Bibliotecas
Convocatorias
Identidad U.N.
Publicado
Framework for Big Data integration in e-government
Marco de referencia para la integración de Big Data en gobierno electrónico
DOI:
https://doi.org/10.15446/dyna.v86n209.77902Palabras clave:
big data, e-government, integration, framework, reference (en)big data, e-gobierno, e-government, integración, marco, referencia (es)
Descargas
This article describes researches regarding Big Data integration in e‑government decision‑making, for instance, in areas like solar energy provisioning, environmental protection, agricultural and natural resources exploitation, health and social care, education, housing and transportation management, among others. These studies refer to regions that have integrated Big Data in e‑government, where South America is still in the early adoption stages. Hence, this study proposes three steppingstones for Big Data integration in e‑government decision‑making, production, management and application. The proposed framework aims to be a reference in South America for Big Data adoption in e‑government and thus help to mitigate the technology delay regarding other regions. Finally, a case study with open data obtained from the Instituto Nacional de Estadística y Censos of Ecuador (Ecuadorian Statistics and Census Agency) is presented.
En el presente artículo se describen algunos estudios que incorporan Big Data en la toma de decisiones de gobierno electrónico como, por ejemplo, provisión de energía solar, protección del ambiente, producción agrícola, explotación de petróleo, gestión de salud, educación, vivienda y transporte, entre otros. Estos estudios corresponden a regiones que han integrado Big Data en gobierno electrónico. Sudamérica se encuentra aún en proceso de adopción. Por esta razón, el presente estudio propone un marco de integración de Big Data en la toma de decisiones de gobierno electrónico, que consta de tres etapas: la producción, la gestión y la aplicación de Big Data. El marco propuesto pretende servir de referencia para la adopción de Big Data en el gobierno electrónico y así ayudar a disminuir el retraso de Sudamérica con respecto a otras regiones. Finalmente, se presenta un estudio de caso en Ecuador, con datos abiertos del banco de datos del Instituto Nacional de Estadística y Censos.
Referencias
Assunção, M. et al., Big Data computing and clouds: trends and future
directions, Journal of Parallel and Distributed Computing, 79, pp. 3-
, 2016. DOI: 10.1016/j.jpdc.2014.08.003
Khalifa, S. et al., The six pillars for building Big Data analytics
ecosystems, ACM Computing Surveys, 49(2), pp. 33:1-33:35, 2016.
DOI: 10.1145/2963143
Seo, J.Y. and Lee, H.M., A study on statistical map of air pollution in
Korea using R, Proceedings of the 4th International Conference on
Computer Applications and Information Processing Technology,
, pp. 1-5. DOI: 10.1109/CAIPT.2017.8320675
Zuo, J. et al., Using Big Data from air quality monitors to evaluate
indoor PM 2.5 exposure in buildings: case study in Beijing,
Enviromental Pollution, 240, pp. 839-847, 2018. DOI:
1016/j.envpol.2018.05.030
Kim, P.W., Operating an environmentally sustainable city using fine
dust level Big Data measured at individual elementary schools,
Sustainable Cities and Society, 37, pp. 1-6, 2018, DOI:
1016/j.scs.2017.10.019
Abella, A., Ortiz-de-Urbina-Criado, M. and De-Pablos-Heredero, C.,
A model for the analysis of data-driven innovation and value
generation in smart cities’ ecosystems, Cities, 64, pp. 47-53, 2017.
DOI: 10.1016/j.cities.2017.01.011
Latin American Big Data and analytics market, forecast to 2023,
Research and Markets, [online]. 2018. Available at:
https://www.researchandmarkets.com/research/vdwzt9/latin_america
n?w=4
Hwang, T., How big and open data can transform Latin America,
World Economy Forum on Latin America, [online]. 2018. Available
at: https://www.weforum.org/agenda/2018/03/latin-america-smartcities-
big-data/
United Nations. World population projected to reach 9.8 billion in
, and 11.2 billion in 2100, [online]. 2017. Available at:
https://www.un.org/development/desa/en/news/population/worldpopulation-
prospects-2017.html
Escudero, S., Big Data como mejora competitiva para la gestión de la
información en la Agricultura Argentina, Universidad de San Andrés,
[en línea]. 2015. Disponible en:
http://repositorio.udesa.edu.ar/jspui/handle/10908/10919
Torres, J.F., Troncoso, A., Koprinska, I., Wang, Z. and MartínezÁlvarez,
F., Deep learning for Big Data time series forecasting
applied to solar power, Proceedings of the 13th International
Conference on Soft Computing Models in Industrial and
Environmental Applications, pp. 123-133, 2018. DOI: 10.1007/978-
-319-94120-2_12
Ghaemi, Z., Alimohammadi, A. and Farnaghi, M., LaSVM-based Big
Data learning system for dynamic prediction of air pollution in
Tehran, Environmental Monitoring and Assessment, 190(5), pp. 1-17,
DOI: 10.1007/s10661-018-6659-6
Kuo, C., Pan, R.H., Chan, C.K., Wu, C., Phan, D.V. and Chan, C.L.,
Application of a time-stratified case-crossover design to explore the
effects of air pollution and season on childhood asthma
hospitalization in cities of differing urban patterns: Big Data analytics
of government open data, International Journal of Environmental
Research and Public Health, 15(4), pp. 1-15, 2018. DOI:
3390/ijerph15040647
Bougoudis, I., Demertzis, K., Iliadis, L., Anezakis, V.D. and
Papaleonidas, A., FuSSFFra, a fuzzy semi-supervised forecasting
framework: the case of the air pollution in Athens, Neural Computing
and Applications, 29(7), pp. 375-388, 2018. DOI: 10.1007/s00521-
-3125-2
Lu, H., Song, J., Di, T.J., Kurdestany, M. and Wang, H., A deep belief
network based model for urban haze prediction, Tehnički vjesnik,
(2), pp. 519-527, 2018. DOI: 10.17559/TV-20180204162632
Wu, Y., Zhang, W., Sheng, J., Mo, Z. and Peng, Y., Smart city with
Chinese characteristics against the background of big data: idea,
action and risk, Journal of Cleaner Production, 173, pp. 60-66, 2018.
DOI: 10.1016/j.jclepro.2017.01.047
Zaree, T. and Honarvar, A.R., Improvement of air pollution prediction
in a smart city and its correlation with weather conditions using
metrological big data, Journal of Electrical Engineering and
Computer Sciences, 26(3), pp. 1302-1313, 2018.
Uhomoibhi, J. and White, C., Data visualization and enhanced
learning in engineering education through oil pollution studies and
environmental impact assessment, Proceedings of the International
Conference on Interactive Collaborative Learning, pp. 271-281, 2017.
DOI: 10.1007/978-3-319-73204-6_31
Kim, C., Park, S.H. and Ha, Y.G., Correlation analysis between
vehicular traffic and PM using sensor Big Data, Proceedings of the
IEEE International Conference on Big Data and Smart Computing,
pp. 644-648, 2018. DOI: 10.1109/BigComp.2018.00116
Jiang, P., Liu, L., Li, H. and Shi, Y., Congestion prediction of urban
traffic employing SRBDP, Proceedings of the IEEE International
Symposium on Parallel and Distributed Processing with Applications,
pp. 1099-1106, 2017. DOI: 10.1109/ISPA/IUCC.2017.00166
Santi D. et al., Seasonal variation of semen parameters correlates with
environmental temperature and air pollution: a Big Data analysis over
years, Environmental Pollution, 235, pp. 806-813, 2018. DOI:
1016/j.envpol.2018.01.021
Zhang, Y. and Mi, Z., Environmental benefits of bike sharing: a big
databased analysis, Applied Energy, 220, pp. 296-301, 2018. DOI:
1016/j.apenergy.2018.03.101
Cheng, J. and Liu, Y., The effects of public attention on the
environmental performance of high-polluting firms: based on big data
from web search in China, Journal of Cleaner Production, 186, pp.
-341, 2018. DOI: 10.1016/j.jclepro.2018.03.146
Jinbo, C., Yu, Z. and Lam, A., Research on monitoring platform of
agricultural product circulation efficiency supported by cloud
computing, Wireless Personal Communications, 102(4), pp. 3573-
, 2018. DOI: 10.1007/s11277-018-5392-3
Wright, N.H., Tracking corporate stewardship of environmental
services-opportunities of Big Data applications and long term value,
Proceedings of the International Conference and Exhibition on
Health, Safety, Security, Environment, and Social Responsibility, pp.
-7, 2018. DOI: 10.2118/190572-MS
Yang, L., A literature review of the impact of corruption on economic
growth based on Big Data, Boletín Técnico, 55(19), pp. 561-569,
Holm, S. and Ploug, T., Big Data and health research-The governance
challenges in a mixed data economy, Journal of Bioethical Inquiry,
(4), pp. 515-525, 2017. DOI: 10.1007/s11673-017-9810-0
Tseng, F., Hsueh, J., Tseng, C., Yang, Y., Chao, H. and Chou, L.,
Congestion prediction with Big Data for real-time highway traffic,
Martinez-Mosquera & Luján-Mora / Revista DYNA, 86(209), pp. 215-224, April - June, 2019.
IEEE Access, 6, pp. 57311-57323, 2018. DOI:
1109/ACCESS.2018.2873569
Sánchez, L., Vásquez, C., Viloria, A. and Meza-Estrada, C.,
Conglomerates of Latin American countries and public policies for
the sustainable development of the electric power generation sector.
Proceedings of the International Conference in Data Mining and Big
Data, pp. 759-766, 2018. DOI: 10.1007/978-3-319-93803-5_71
Lopez, F., Bonfante, M., Gonzalez, I. and Muñoz, R., IoT and Big
Data in public health: a case study in Colombia, Protocols and
Applications for the Industrial Internet of Things, pp. 309-321, 2018.
DOI: 10.4018/978-1-5225-3805-9
Trujillo, J. and Luján-Mora, S., A UML based approach for modeling
ETL processes in data warehouses. Proceedings of the 22nd
International Conference on Conceptual Modeling, pp. 307-320,
DOI: 10.1007/978-3-540-39648-2_25
Luján-Mora, S. and Palomar, M., Reducing inconsistency in
integrating data from different sources. Proceedings of the
International Database Engineering and Applications Symposium, pp.
-218, 2001. DOI: 10.1109/IDEAS.2001.938087
Object Management Group, Model Driven Architecture (MDA)
MDA Guide rev. 2.0. [online]. Available at:
Coto, A., Elizabeth, Y., y Martínez, A.E., La gestión de desechos de
luminarias, análisis de los casos de Brasil y España. Perspectivas para
El Salvador según diagnóstico de uso y disposición, Tesis de grado.
Universidad Don Bosco, 2011, pp. 1-28.
Henriksen, H.Z., One step forward and two steps back: E-government
policies in practice. policy analytics, modelling, and informatics.
Public Administration and Information Technology, 25, Springer,
Cham, 2018, DOI: 10.1007/978-3-319-61762-6_4
Bertot, J., Gorham, U., Jaeger, P., Sarin, L. and Choi, H., Big data,
open government and e-government: issues, policies and
recommendations, Information Polity, 19(1,2), pp. 5-16, 2014. DOI:
3233/IP-140328
Bertot, J. and Choi, H., Big data and e-government: issues, policies,
and recommendations. Proceedings of the 14th Annual International
Conference on Digital Government Research, pp. 1-10, 2013. DOI:
1145/2479724.2479730
Yu-Che, C. and Tsui-Chuan, H., Big Data for Digital government:
opportunities, challenges, and strategies, International Journal of
Public Administration in the Digital Age, 1(1), pp. 1-14, 2014. DOI:
4018/ijpada.2014010101
Gang-Hoon, K., Silvana, T. and Ji-Hyong, C., Big-Data applications
in the government sector, Communications of the ACM, 57(3), pp.
-85, 2014. DOI: 10.1145/2500873
Consejo Nacional de Planificación del Ecuador. Plan Nacional de
Desarrollo 2017-2021, [en línea]. 2017. Disponible en:
http://www.planificacion.gob.ec/wpcontent/
uploads/downloads/2017/10/PNBV-26-OCTFINAL_
K.compressed1.pdf
Morán, C. and Cañarte, T., Las PYMEs y su incorporación en las
TICs, Manta, Ecuador, Dominio de las Ciencias, 3(3), pp. 734-741,
Autor corporativo: Revista Líderes. El big data le hace un zoom al
consumidor. Revista Líderes. [en línea]. 2013. Disponible en:
https://www.revistalideres.ec/lideres/big-data-le-zoomconsumidor.
html
Romero, D., El ‘big data’ cambia los negocios en la plaza Foch, en
Quito. Diario EL COMERCIO. ElComercio.com. [en línea]. 2018.
Disponible en: https://www.elcomercio.com/actualidad/bigdatacambia-
negocios-plaza-foch.html.
Brobst, S., Las empresas que no utilicen Big Data no sobrevivirán en
la economía moderna, ComputerWorld, [en línea]. 2018. Disponible
en: http://www.computerworld.com.ec/actualidad/tendencias/242-
stephen-brobst.html
Secretaría Nacional de la Administración Pública. Guía de política
pública de datos abiertos. [en línea]. 2014. Disponible en:
https://www.gobiernoelectronico.gob.ec/wpcontent/
uploads/2018/10/Gu%C3%ADa-de-pol%C3%ADticap%
C3%BAblica-de-Datos-Abiertos.pdf
Jauk, S., Kramer, D. and Leodolter, W., Cleansing and imputation of
body mass index data and its impact on a machine learning based
prediction model. Proceedings of the 12th eHealth Conference, pp.
-123, 2018. DOI: 10.3233/978-1-61499-858-7-116
Krause, R.W., Huisman, M., Steglich, C. and Sniiders, T.A., Missing
network data a comparison of different imputation methods.
Proceedings of IEEE/ACM International Conference on Advances in
Social Networks Analysis and Mining, pp. 159-163, 2018. DOI:
1109/ASONAM.2018.8508716
Iribarme, R., Tratamiento de sólidos, los residuos en el contexto de la
luminotecnia. [en línea]. 2002. Sicanews. Disponible en:
http://www.paginadigital.com.ar/articulos/2002rest/2002terc/tecnolo
gia/sica92.html
Cómo citar
IEEE
ACM
ACS
APA
ABNT
Chicago
Harvard
MLA
Turabian
Vancouver
Descargar cita
CrossRef Cited-by
1. Dharen Kumar Pandey, Ahmed Imran Hunjra, Ratikant Bhaskar, Mamdouh Abdulaziz Saleh Al-Faryan. (2023). Artificial intelligence, machine learning and big data in natural resources management: A comprehensive bibliometric review of literature spanning 1975–2022. Resources Policy, 86, p.104250. https://doi.org/10.1016/j.resourpol.2023.104250.
2. Diana Martinez-Mosquera, Sergio Luján-Mora. (2019). Framework for Big Data integration in e-government. DYNA, 86(209), p.215. https://doi.org/10.15446/dyna.v86n209.77902.
3. Shirley Kempeneer, Frederik Heylen. (2023). Virtual state, where are you? A literature review, framework and agenda for failed digital transformation. Big Data & Society, 10(1) https://doi.org/10.1177/20539517231160528.
4. Manuel Ayala-Chauvin, Fátima Avilés-Castillo, Jorge Buele. (2023). Exploring the Landscape of Data Analysis: A Review of Its Application and Impact in Ecuador. Computers, 12(7), p.146. https://doi.org/10.3390/computers12070146.
5. Shirley Kempeneer, Frederik Heylen. (2024). Virtual State, Where Are You? A Literature Review, Framework and Agenda for Failed Digital Transformation. SSRN Electronic Journal, https://doi.org/10.2139/ssrn.4982340.
Dimensions
PlumX
Visitas a la página del resumen del artículo
Descargas
Licencia
Derechos de autor 2019 DYNA
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
El autor o autores de un artículo aceptado para publicación en cualquiera de las revistas editadas por la facultad de Minas cederán la totalidad de los derechos patrimoniales a la Universidad Nacional de Colombia de manera gratuita, dentro de los cuáles se incluyen: el derecho a editar, publicar, reproducir y distribuir tanto en medios impresos como digitales, además de incluir en artículo en índices internacionales y/o bases de datos, de igual manera, se faculta a la editorial para utilizar las imágenes, tablas y/o cualquier material gráfico presentado en el artículo para el diseño de carátulas o posters de la misma revista.
