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dc.contributor.advisorMartín Bautista, María José 
dc.contributor.advisorRuiz Jiménez, María Dolores 
dc.contributor.authorFernández Basso, Carlos Jesús 
dc.contributor.otherUniversidad de Granada.es_ES
dc.contributor.otherUniversidad de Granada. Programa de Doctorado en Tecnologías de la Información y la Comunicaciónes_ES
dc.date.accessioned2020-10-27T09:05:44Z
dc.date.available2020-10-27T09:05:44Z
dc.date.issued2020
dc.date.submitted2020-07-08
dc.identifier.citationFernández Basso, Carlos Jesús. Reglas de asociación difusas en Big Data. Granada: Universidad de Granada, 2020. [http://hdl.handle.net/10481/63902]es_ES
dc.identifier.isbn978-84-1306-634-9
dc.identifier.urihttp://hdl.handle.net/10481/63902
dc.description.abstractThis paper has reviewed the field of Data Science and how Data Science techniques can be applied to building energy management. Specifically, we have focused on building operation, energy load prediction, and identification of consumption patterns. Our experiments show that Big Data technologies can solve the computational problems that appear when processing of large amounts of data, which are likely to have an increasing relevance with the advent of the Internet of Things –with smart meters and appliances fully connected to the Internet. However, the applications to real-world scenarios are still scarce. In our experience, one of the most important aspects to improve is achieving a greater involvement of the building managers in the data analysis process. To do this, future research work should explore two complementary directions, namely, showing the potential of Data Science to building managers, and developing more user-friendly algorithms and tools. In this way, we expect that new approaches will be less opaque, easier to use, more customizable, and above all other features, more engaging.es_ES
dc.description.sponsorshipTesis Univ. Granada.es_ES
dc.description.sponsorshipSpanish Ministries of Science, Innovation and Universities (TIN2017-91223-EXP)es_ES
dc.description.sponsorshipEconomy and Competitiveness (TIN2015-64776-C3-1-R)es_ES
dc.description.sponsorshipEnergy IN TIME project from the EU 7o Framework Programme (grant agreement No. 608981)es_ES
dc.description.sponsorshipCOPKIT project from the EU 8 Framework Programme (H2020) researches_ES
dc.description.sponsorshipInnovation programme (grant agreement No 786687)es_ES
dc.description.sponsorshipUniversity of Granada (Programa de Proyectos para la incorporación de jóvenes doctores a nuevas líneas de investigación)es_ES
dc.description.sponsorshipSpanish Government (TIN2012-30939 project)es_ES
dc.format.mimetypeapplication/pdfen_US
dc.language.isoenges_ES
dc.language.isospaes_ES
dc.publisherUniversidad de Granadaes_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectBig Dataes_ES
dc.subjectReglas de asociaciónes_ES
dc.titleReglas de asociación difusas en Big Dataes_ES
dc.title.alternativeFuzzy association rules in Big Dataes_ES
dc.typedoctoral thesises_ES
europeana.typeTEXTen_US
europeana.dataProviderUniversidad de Granada. España.es_ES
europeana.rightshttp://creativecommons.org/licenses/by-nc-nd/3.0/en_US
dc.rights.accessRightsopen accesses_ES
dc.type.hasVersionVoRes_ES


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