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dc.creatorSilva, Jesús
dc.creatorHernández, Lissette
dc.creatorVarela, Noel
dc.creatorPineda Lezama, Omar Bonerge
dc.creatorTafur Cabrera, Jorge
dc.creatorLucena León Castro, Bellanith Ruth
dc.creatorRedondo Bilbao, Osman
dc.creatorPérez Coronel, Leidy
dc.date.accessioned2019-08-08T14:36:31Z
dc.date.available2019-08-08T14:36:31Z
dc.date.issued2019-06-26
dc.identifier.isbn978-3-030-22807-1
dc.identifier.isbn978-3-030-22808-8
dc.identifier.urihttp://hdl.handle.net/11323/5132
dc.description.abstractIn the academic world, a large amount of data is handled each day, ranging from student’s assessments to their socio-economic data. In order to analyze this historical information, an interesting alternative is to implement a Data Warehouse. However, Data Warehouses are not able to perform predictive analysis by themselves, so machine intelligence techniques can be used for sorting, grouping, and predicting based on historical information to improve the analysis quality. This work describes a Data Warehouse architecture to carry out an academic performance analysis of students.es_ES
dc.language.isoenges_ES
dc.publisherInternational Symposium on Neural Networkses_ES
dc.relation.ispartofhttps://doi.org/10.1007/978-3-030-22808-8_20es_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.subjectIntelligent data retrievales_ES
dc.subjectData Warehousees_ES
dc.subjectUnique Identification Numberes_ES
dc.subjectAcademic performancees_ES
dc.titleIntelligent and Distributed Data Warehouse for Student’s Academic Performance Analysises_ES
dc.typePreprintes_ES
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