Benchmarking business analytics techniques in Big Data
| dc.contributor.author | Oliveira, Catia | por |
| dc.contributor.author | Guimarães, Tiago André Saraiva | por |
| dc.contributor.author | Portela, Filipe | por |
| dc.contributor.author | Santos, Manuel | por |
| dc.date.accessioned | 2020-10-29T22:42:08Z | |
| dc.date.available | 2020-10-29T22:42:08Z | |
| dc.date.issued | 2019 | |
| dc.date.updated | 2020-10-28T11:07:55Z | |
| dc.description.abstract | Technological developments and the growing dependence of organizations and society in the world of the internet led to the growth and variety of data. This growth and variety have become a challenge to the traditional techniques of Business Analytics. In this project, we conducted a benchmarking process that aimed to assess the performance of some Data Mining tools, like RapidMiner, in Big Data environment. Firstly, was analyzed a study where a group of Data Mining tools are evaluated and determined what is the best Data Mining tool, according to the evaluation criteria. After that, the best two tools considered in the study are analyzed regarding their ability to analyze data in a Big Data environment. Finally, studies were carried out on the evaluations of the RapidMiner and KNIME tools for their performance in the Big Data environment. | por |
| dc.description.sponsorship | This work has been supported by national funds through FCT -Fundacao para a Ciencia e Tecnologia within the Project Scope: UID/CEC/00319/2019 and Deus ex Machina (DEM): Symbiotic technology for societal efficiency gains -NORTE-01-0145-FEDER-000026. | por |
| dc.distribution | international | por |
| dc.identifier.doi | 10.1016/j.procs.2019.11.026 | por |
| dc.identifier.issn | 1877-0509 | |
| dc.identifier.uri | https://hdl.handle.net/1822/67912 | |
| dc.language.iso | eng | por |
| dc.peerreviewed | yes | por |
| dc.publisher | Elsevier Science | por |
| dc.relation | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FCEC%2F00319%2F2019/PT | por |
| dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S1877050919317260 | por |
| dc.rights | openAccess | por |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | por |
| dc.subject | Big Data | por |
| dc.subject | Analytics | por |
| dc.subject | Data Mining | por |
| dc.subject | Benchmarking | por |
| dc.subject.fos | Ciências Naturais::Ciências da Computação e da Informação | por |
| dc.subject.fos | Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática | por |
| dc.subject.wos | Science & Technology | |
| dc.title | Benchmarking business analytics techniques in Big Data | por |
| dc.type | conferencePaper | por |
| dspace.entity.type | Publication | en |
| oaire.citationEndPage | 695 | por |
| oaire.citationStartPage | 690 | por |
| oaire.citationVolume | 160 | por |
| oaire.version | VoR | por |
| sdum.conferencePublication | 10TH INT CONF ON EMERGING UBIQUITOUS SYST AND PERVAS NETWORKS (EUSPN-2019) / THE 9TH INT CONF ON CURRENT AND FUTURE TRENDS OF INFORMAT AND COMMUN TECHNOLOGIES IN HEALTHCARE (ICTH-2019) / AFFILIATED WORKOPS | por |
| sdum.export.identifier | 7389 | |
| sdum.journal | Procedia Computer Science | por |
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