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Algorithms for the control of key performance indicators for smart cities

dc.contributor.authorSilva, Jesusspa
dc.contributor.authorMojica, Juliospa
dc.contributor.authorPiñeres Castillo, Aurora Patriciaspa
dc.contributor.authorRojas, Rafaelspa
dc.contributor.authorAcosta, Sandraspa
dc.contributor.authorGarcia Guliany, Jesusspa
dc.contributor.authorSteffens Sanabria, Ernestospa
dc.date.accessioned2021-01-28T20:00:04Z
dc.date.available2021-01-28T20:00:04Z
dc.date.issued2020
dc.identifier.urihttps://hdl.handle.net/11323/7789spa
dc.description.abstractIn 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.spa
dc.format.mimetypeapplication/pdfspa
dc.language.isoeng
dc.publisherCorporación Universidad de la Costaspa
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/spa
dc.sourceProcedia Computer Sciencespa
dc.subjectsmart citiesspa
dc.subjectopen data for cities evaluationspa
dc.subjectJSON documents of keyspa
dc.subjectperformance indicatorsspa
dc.subjectNOSQLspa
dc.titleAlgorithms for the control of key performance indicators for smart citiesspa
dc.typeArtículo de revistaspa
dc.source.urlhttps://www.sciencedirect.com/science/article/pii/S1877050920305378#!spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.doihttps://doi.org/10.1016/j.procs.2020.03.099spa
dc.identifier.instnameCorporación Universidad de la Costaspa
dc.identifier.reponameREDICUC - Repositorio CUCspa
dc.identifier.repourlhttps://repositorio.cuc.edu.co/spa
dc.relation.references1 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)spa
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dc.relation.references5 Cohen, B.: Boyd Cohen, https://www.smart-circle.org/smartcity/blog/boyd-cohen-the- smart-city-wheel/37120, S.I.D.: ISO37120spa
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dc.relation.references9 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)spa
dc.relation.referencesHerrmann 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)spa
dc.relation.references11 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.spa
dc.relation.references12 Viloria Amelec, et al. Integration of Data Mining Techniques to PostgreSQL Database Manager System Procedia Computer Science, 155 (2019), pp. 575-580spa
dc.relation.references13 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)spa
dc.relation.references14 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)spa
dc.relation.references15 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_23spa
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dc.type.coarhttp://purl.org/coar/resource_type/c_6501spa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/articlespa
dc.type.redcolhttp://purl.org/redcol/resource_type/ARTspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.rights.coarhttp://purl.org/coar/access_right/c_abf2spa


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