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A Scalable Machine Learning Online Service for Big Data Real-Time Analysis

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Computación Evolutiva y Redes Neuronales (EVANNAI)es
dc.contributor.authorBaldominos Gómez, Alejandroes
dc.contributor.authorAlbacete García, Esperanzaes
dc.contributor.authorSáez Achaerandio, Yagoes
dc.contributor.authorIsasi, Pedroes
dc.date.accessioned2015-05-19T09:39:49Z
dc.date.available2015-05-19T09:39:49Z
dc.date.issued2014-12
dc.descriptionProceedings of: IEEE Symposium Series on Computational Intelligence (SSCI 2014). Orlando, FL, USA, December 09-12, 2014.en
dc.description.abstractThis work describes a proposal for developing and testing a scalable machine learning architecture able to provide real-time predictions or analytics as a service over domain-independent big data, working on top of the Hadoop ecosystem and providing real-time analytics as a service through a RESTful API. Systems implementing this architecture could provide companies with on-demand tools facilitating the tasks of storing, analyzing, understanding and reacting to their data, either in batch or stream fashion; and could turn into a valuable asset for improving the business performance and be a key market differentiator in this fast pace environment. In order to validate the proposed architecture, two systems are developed, each one providing classical machine-learning services in different domains: the first one involves a recommender system for web advertising, while the second consists in a prediction system which learns from gamers' behavior and tries to predict future events such as purchases or churning. An evaluation is carried out on these systems, and results show how both services are able to provide fast responses even when a number of concurrent requests are made, and in the particular case of the second system, results clearly prove that computed predictions significantly outperform those obtained if random guess was used.en
dc.description.sponsorshipThis research work is part of Memento Data Analysis project, co-funded by the Spanish Ministry of Industry, Energy and Tourism with identifier TSI-020601-2012-99.en
dc.description.statusPublicadoes
dc.format.extent8
dc.format.mimetypeapplication/pdf
dc.identifier.bibliographicCitation2014 IEEE Symposium on Computational Intelligence in Big Data (CIBD): proceedings (2014). IEEE, 1-8.en
dc.identifier.doi10.1109/CIBD.2014.7011537
dc.identifier.isbn978-1-4799-4541-2
dc.identifier.publicationfirstpage1
dc.identifier.publicationlastpage8
dc.identifier.publicationtitle2014 IEEE Symposium on Computational Intelligence in Big Data (CIBD): proceedingsen
dc.identifier.urihttps://hdl.handle.net/10016/20755
dc.identifier.uxxiCC/0000022935
dc.language.isoengen
dc.publisherIeee - The Institute Of Electrical And Electronics Engineers, Incen
dc.relation.eventdateDecember, 19-12 2014en
dc.relation.eventplaceOrlando, FL, USA.en
dc.relation.eventtitleIEEE Symposium Series on Computational Intelligence (SSCI 2014)en
dc.relation.publisherversionhttp://dx.doi.org/10.1109/CIBD.2014.7011537es
dc.rights© 2014 IEEE.en
dc.rights.accessRightsopen accesses
dc.subject.ecienciaInformáticaes
dc.subject.otherBig dataen
dc.subject.otherInterneten
dc.subject.otherAdvertising data processingen
dc.subject.otherBusiness data processingen
dc.subject.otherLearning (artificial intelligence)en
dc.subject.otherPurchasingen
dc.subject.otherRecommender systemsen
dc.titleA Scalable Machine Learning Online Service for Big Data Real-Time Analysisen
dc.typeconference paper*
dc.type.hasVersionAM*
dspace.entity.typePublication
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