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dc.contributor.authorGoncalves, Andrepor
dc.contributor.authorPortela, Filipepor
dc.contributor.authorSantos, Manuelpor
dc.contributor.authorRua, Fernandopor
dc.date.accessioned2020-10-28T18:34:57Z-
dc.date.available2020-10-28T18:34:57Z-
dc.date.issued2017-
dc.identifier.issn1877-0509-
dc.identifier.urihttps://hdl.handle.net/1822/67833-
dc.description.abstractThese days the exponential increase in the volume and variety of data stored by companies and organizations of various sectors of activity, has required to organizations the search for new solutions to improve their services and/or products, taking advantage of technological evolution. As a response to the inability of organizations to process large quantities and varieties of data, in the technological market, arise the Big Data. This emerging concept defined mainly by the volume, velocity and variety has evolved greatly in part by its ability to generate value for organizations in decision making. Currently, the health care sector is one of the five sectors of activity where the potential of Big Data growth most stands out. However, the way to go is still long and in fact there are few organizations, related to health care, that are taking advantage of the true potential of Big Data. The main target of this research is to produce a real-time Big Data architecture to the INTCare system, of the Centro Hospitalar do Porto, using the main open source big data solution, the Apache Hadoop. As a result of the first phase of this research we obtained a generic architecture who can be adopted by other Intensive Care Units.por
dc.description.sponsorship"This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundacao para a Ciencia e Tecnologia within the Project Scope: UID/CEC/00319/2013." This work is also supported by the Deus ex Machina (DEM): Symbiotic technology for societal efficiency gains - NORTE-01-0145-FEDER-000026por
dc.language.isoengpor
dc.publisherElsevier Science BVpor
dc.relationinfo:eu-repo/grantAgreement/FCT/5876/147280/PTpor
dc.rightsopenAccesspor
dc.subjectIntensive Medicinepor
dc.subjectIntensive Care Unitspor
dc.subjectReal-timepor
dc.subjectBig Datapor
dc.subjectArchitecturepor
dc.subjectHadooppor
dc.titleTowards of a real-time Big Data architecture to intensive carepor
dc.typeconferencePaperpor
dc.peerreviewedyespor
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S1877050917317039por
oaire.citationStartPage585por
oaire.citationEndPage590por
oaire.citationVolume113por
dc.date.updated2020-10-28T12:09:40Z-
dc.identifier.doi10.1016/j.procs.2017.08.294por
dc.subject.fosCiências Naturais::Ciências da Computação e da Informaçãopor
dc.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informáticapor
dc.subject.wosScience & Technology-
sdum.export.identifier7414-
sdum.journalProcedia Computer Sciencepor
sdum.conferencePublication8TH INTERNATIONAL CONFERENCE ON EMERGING UBIQUITOUS SYSTEMS AND PERVASIVE NETWORKS (EUSPN 2017) / 7TH INTERNATIONAL CONFERENCE ON CURRENT AND FUTURE TRENDS OF INFORMATION AND COMMUNICATION TECHNOLOGIES IN HEALTHCARE (ICTH-2017) / AFFILIATED WORKSHOPSpor
sdum.bookTitle8TH INTERNATIONAL CONFERENCE ON EMERGING UBIQUITOUS SYSTEMS AND PERVASIVE NETWORKS (EUSPN 2017) / 7TH INTERNATIONAL CONFERENCE ON CURRENT AND FUTURE TRENDS OF INFORMATION AND COMMUNICATION TECHNOLOGIES IN HEALTHCARE (ICTH-2017) / AFFILIATED WORKSHOPSpor
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