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dc.contributor.authorCardinale, Yudith
HAL ID: 740074
ORCID: 0000-0002-5966-0113
dc.contributor.authorGuehis, Sonia
dc.contributor.authorRukoz, Marta
dc.date.accessioned2019-07-09T10:57:32Z
dc.date.available2019-07-09T10:57:32Z
dc.date.issued2017
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/19208
dc.descriptionCommunications in Computer and Information Science book series (CCIS, volume 868) Revised Selected Papers
dc.language.isoenen
dc.subjectBig Data Analytic
dc.subjectAnalytic models for big data
dc.subjectAnalytical data management applications
dc.subject.ddc005en
dc.titleClassifying Big Data Analytic Approaches: A Generic Architecture
dc.typeCommunication / Conférence
dc.contributor.editoruniversityotherUniversida Simon Bolivar
dc.contributor.editoruniversityotherUniversité Paris X Nanterre
dc.description.abstractenThe explosion of the huge amount of generated data to be analyzed by several applications, imposes the trend of the moment, the Big Data boom, which in turn causes the existence of a vast landscape of architectural solutions. Non expert users who have to decide which analytical solutions are the most appropriates for their particular constraints and specific requirements in a Big Data context, are today lost, faced with a panoply of disparate and diverse solutions. To support users in this hard selection task, in a previous work, we proposed a generic architecture to classify Big Data Analytical Approaches and a set of criteria of comparison/evaluation. In this paper, we extend our classification architecture to consider more types of Big Data analytic tools and approaches and improve the list of criteria to evaluate them. We classify different existing Big Data analytics solutions according to our proposed generic architecture and qualitatively evaluate them in terms of the criteria of comparison. Additionally, we propose a preliminary design of a decision support system, intended to generate suggestions to users based on such classification and on a qualitative evaluation in terms of previous users experiences, users requirements, nature of the analysis they need, and the set of evaluation criteria.
dc.identifier.citationpages268-295
dc.relation.ispartoftitleSoftware Technologies,12th International Joint Conference (ICSOFT 2017)
dc.relation.ispartofeditorEnrique Cabello, Jorge Cardoso, Leszek A. Maciaszek, Marten van Sinderen
dc.relation.ispartofpublnameSpringer International Publishing
dc.relation.ispartofpublcityBerlin Heidelberg
dc.relation.ispartofurl10.1007/978-3-319-93641-3
dc.contributor.countryeditoruniversityotherVENEZUELA, BOLIVARIAN REPUBLIC OF
dc.subject.ddclabelProgrammation, logiciels, organisation des donnéesen
dc.relation.ispartofisbn978-3-319-93640-6
dc.relation.conftitleSoftware Technologies,12th International Joint Conference (ICSOFT 2017)
dc.relation.confdate2017
dc.relation.confcityBerlin Heidelberg
dc.relation.forthcomingnonen
dc.identifier.doi10.1007/978-3-319-93641-3_13
dc.description.ssrncandidatenon
dc.description.halcandidateoui
dc.description.readershiprecherche
dc.description.audienceInternational
dc.date.updated2022-12-08T19:36:32Z
hal.faultCode{"duplicate-entry":{"hal-02096456":{"doi":"1.0"}}}


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