Algorithms for the control of key performance indicators for smart cities
In addition to the increase in the population in cities, there is an increase in the demand for resources and services, and phenomena such as the lack of social inclusion and inequity appear. In order to mitigate these problems, Smart Cities propose the development of measurement strategies that sup...
- Autores:
-
Silva, Jesus
Mojica, Julio
Piñeres Castillo, Aurora Patricia
Rojas, Rafael
Acosta, Sandra
Garcia Guliany, Jesus
Steffens Sanabria, Ernesto
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7789
- Acceso en línea:
- https://hdl.handle.net/11323/7789
https://doi.org/10.1016/j.procs.2020.03.099
https://repositorio.cuc.edu.co/
- Palabra clave:
- smart cities
open data for cities evaluation
JSON documents of key
performance indicators
NOSQL
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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Algorithms for the control of key performance indicators for smart cities |
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Algorithms for the control of key performance indicators for smart cities |
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Algorithms for the control of key performance indicators for smart cities smart cities open data for cities evaluation JSON documents of key performance indicators NOSQL |
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Algorithms for the control of key performance indicators for smart cities |
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Algorithms for the control of key performance indicators for smart cities |
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Algorithms for the control of key performance indicators for smart cities |
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Algorithms for the control of key performance indicators for smart cities |
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Algorithms for the control of key performance indicators for smart cities |
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Silva, Jesus Mojica, Julio Piñeres Castillo, Aurora Patricia Rojas, Rafael Acosta, Sandra Garcia Guliany, Jesus Steffens Sanabria, Ernesto |
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Silva, Jesus Mojica, Julio Piñeres Castillo, Aurora Patricia Rojas, Rafael Acosta, Sandra Garcia Guliany, Jesus Steffens Sanabria, Ernesto |
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smart cities open data for cities evaluation JSON documents of key performance indicators NOSQL |
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smart cities open data for cities evaluation JSON documents of key performance indicators NOSQL |
| description |
In addition to the increase in the population in cities, there is an increase in the demand for resources and services, and phenomena such as the lack of social inclusion and inequity appear. In order to mitigate these problems, Smart Cities propose the development of measurement strategies that support decision-making, which implies the management of an indefinite number of indicators. This paper presents the design and a prototype that implements the algorithms of a general scheme for the control of key performance indicators for Smart Cities. |
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2020 |
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2021-01-28T20:00:04Z |
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1 Chourabi, H., Nam, T., Gil-Garcia, J.R., Mellouli, S., Pardo, T.A., Scholl, H.: Understanding Smart Cities: An Integrative Framework. In: 45th Hawaii International Conference on System Sciences, pp. 1–9 (2011) 2 Townsend Anthony M. Smart cities: Big data, civic hackers, and the quest for a new utopia, WW Norton & Company (2013) 3 ITU: General specifications and KPIs. Pp. 1–34 (2012) 4 Moonen T., Clark G. The Business of Cities 2013, Jones Lang Lasalle IP, INC (2013) 5 Cohen, B.: Boyd Cohen, https://www.smart-circle.org/smartcity/blog/boyd-cohen-the- smart-city-wheel/37120, S.I.D.: ISO37120 6 Institute for Urban Strategies the Mori Memorial Foundation: Global Power City 2017. (2017) 7 Weidema Bo P., et al. Carbon footprint: a catalyst for life cycle assessment? Journal of industrial Ecology, 12 (1) (2008), pp. 3-6 8 Estrada E., Maciel R., Ochoa A., Bernabe-Loranca B., Oliva D., Larios V. Smart City Visualization Tool for the Open Data Georeferenced Analysis Utilizing Machine Learning International Journal of Combinatorial Optimization Problems and Informatics, 9 (2018), pp. 25-40 9 Viloria A., Lis-Gutiérrez J.P., Angulo M., Godoy A.R.M., Moreno G.C., Kamatkar S.J. Methodology for the Design of a Student Pattern Recognition Tool to Facilitate the Teaching - Learning Process Through Knowledge Data Discovery (Big Data). Tan Y., Shi Y., Tang Q. (Eds.), Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943, Springer, Cham (2018) Herrmann P., Svae A., Svendsen H.H., Blech J.O. Collaborative Model-based Development of a Remote Train Monitoring System, ENASE (2016), pp. 383-390 (April) 11 Kamatkar, S.J., Kamble, A., Viloria, A., Hernández-Fernández, L., & Cali, E.G. (2018, June). Database performance tuning and query optimization. In International Conference on Data Mining and Big Data (pp. 3-11). Springer, Cham. 12 Viloria Amelec, et al. Integration of Data Mining Techniques to PostgreSQL Database Manager System Procedia Computer Science, 155 (2019), pp. 575-580 13 Gaurav, G., Karanjit, S., Ramkumar, K.R.: A detailed analysis of data consistency concepts in data exchange formats (JSON & XML) Presented at the March 21 (2017) 14 Deng, X., Zhang, Y., Kang, B., Wu, J., Sun, X., & Deng, Y.: An application of genetic algorithm for university course timetabling problem, pp. 2119-2122, doi:10.1109/CCDC.2011.5968555 (2011) 15 Soria-Alcaraz Jorge A., Martín C., Héctor P., Sotelo-Figueroa M.A. Comparison of metaheuristic algorithms with a methodology of design for the evaluation of hard constraints over the course timetabling problem, Springer Berlin Heidel- berg, Berlin, Heidelberg (2013), pp. 289-302 doi:10.1007/978-3-642-33021-6_23 16 Viloria Amelec, Lezama Omar Bonerge Pineda Improvements for Determining the Number of Clusters in k-Means for Innovation Databases in SMEs Procedia Computer Science, 151 (2019), pp. 1201-1206 |
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Silva, JesusMojica, JulioPiñeres Castillo, Aurora PatriciaRojas, RafaelAcosta, SandraGarcia Guliany, JesusSteffens Sanabria, Ernesto2021-01-28T20:00:04Z2021-01-28T20:00:04Z2020https://hdl.handle.net/11323/7789https://doi.org/10.1016/j.procs.2020.03.099Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/In addition to the increase in the population in cities, there is an increase in the demand for resources and services, and phenomena such as the lack of social inclusion and inequity appear. In order to mitigate these problems, Smart Cities propose the development of measurement strategies that support decision-making, which implies the management of an indefinite number of indicators. This paper presents the design and a prototype that implements the algorithms of a general scheme for the control of key performance indicators for Smart Cities.Silva, JesusMojica, JulioPiñeres Castillo, Aurora Patricia-will be generated-orcid-0000-0003-2445-8297-600Rojas, RafaelAcosta, SandraGarcia Guliany, JesusSteffens Sanabria, Ernestoapplication/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Procedia Computer Sciencehttps://www.sciencedirect.com/science/article/pii/S1877050920305378#!smart citiesopen data for cities evaluationJSON documents of keyperformance indicatorsNOSQLAlgorithms for the control of key performance indicators for smart citiesArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion1 Chourabi, H., Nam, T., Gil-Garcia, J.R., Mellouli, S., Pardo, T.A., Scholl, H.: Understanding Smart Cities: An Integrative Framework. In: 45th Hawaii International Conference on System Sciences, pp. 1–9 (2011)2 Townsend Anthony M. Smart cities: Big data, civic hackers, and the quest for a new utopia, WW Norton & Company (2013)3 ITU: General specifications and KPIs. Pp. 1–34 (2012)4 Moonen T., Clark G. The Business of Cities 2013, Jones Lang Lasalle IP, INC (2013)5 Cohen, B.: Boyd Cohen, https://www.smart-circle.org/smartcity/blog/boyd-cohen-the- smart-city-wheel/37120, S.I.D.: ISO371206 Institute for Urban Strategies the Mori Memorial Foundation: Global Power City 2017. (2017)7 Weidema Bo P., et al. Carbon footprint: a catalyst for life cycle assessment? Journal of industrial Ecology, 12 (1) (2008), pp. 3-68 Estrada E., Maciel R., Ochoa A., Bernabe-Loranca B., Oliva D., Larios V. Smart City Visualization Tool for the Open Data Georeferenced Analysis Utilizing Machine Learning International Journal of Combinatorial Optimization Problems and Informatics, 9 (2018), pp. 25-409 Viloria A., Lis-Gutiérrez J.P., Angulo M., Godoy A.R.M., Moreno G.C., Kamatkar S.J. Methodology for the Design of a Student Pattern Recognition Tool to Facilitate the Teaching - Learning Process Through Knowledge Data Discovery (Big Data). Tan Y., Shi Y., Tang Q. (Eds.), Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943, Springer, Cham (2018)Herrmann P., Svae A., Svendsen H.H., Blech J.O. Collaborative Model-based Development of a Remote Train Monitoring System, ENASE (2016), pp. 383-390 (April)11 Kamatkar, S.J., Kamble, A., Viloria, A., Hernández-Fernández, L., & Cali, E.G. (2018, June). Database performance tuning and query optimization. In International Conference on Data Mining and Big Data (pp. 3-11). Springer, Cham.12 Viloria Amelec, et al. Integration of Data Mining Techniques to PostgreSQL Database Manager System Procedia Computer Science, 155 (2019), pp. 575-58013 Gaurav, G., Karanjit, S., Ramkumar, K.R.: A detailed analysis of data consistency concepts in data exchange formats (JSON & XML) Presented at the March 21 (2017)14 Deng, X., Zhang, Y., Kang, B., Wu, J., Sun, X., & Deng, Y.: An application of genetic algorithm for university course timetabling problem, pp. 2119-2122, doi:10.1109/CCDC.2011.5968555 (2011)15 Soria-Alcaraz Jorge A., Martín C., Héctor P., Sotelo-Figueroa M.A. Comparison of metaheuristic algorithms with a methodology of design for the evaluation of hard constraints over the course timetabling problem, Springer Berlin Heidel- berg, Berlin, Heidelberg (2013), pp. 289-302 doi:10.1007/978-3-642-33021-6_2316 Viloria Amelec, Lezama Omar Bonerge Pineda Improvements for Determining the Number of Clusters in k-Means for Innovation Databases in SMEs Procedia Computer Science, 151 (2019), pp. 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