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dc.contributor.authorÖlmezoğulları, Erdi
dc.contributor.authorArı, İsmail
dc.date.accessioned2014-11-24T08:02:48Z
dc.date.available2014-11-24T08:02:48Z
dc.date.issued2013
dc.identifier.isbn978-0-7695-5006-0
dc.identifier.urihttp://hdl.handle.net/10679/661
dc.identifier.urihttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6597126&tag=1
dc.descriptionDue to copyright restrictions, the access to the full text of this article is only available via subscription.en_US
dc.description.abstractTo extract useful and actionable information in real-time, the information technology (IT) world is coping with big data problems today. In this paper, we present implementation details and performance results of ReCEPtor, our system for "online" Association Rule Mining (ARM) over big and fast data streams. Specifically, we added Apriori and two different FP-Growth algorithms inside Esper Complex Event Processing (CEP) engine and compared their performances using LastFM social music site data. Our most important findings show that online ARM can generate (1) more unique rules, (2) with higher throughput, and (3) much sooner (lower latency) than offline rule mining. In addition, we have found many interesting and realistic musical preference rules such as "George HarrisonàBeatles". We demonstrate a sustained rate of ~15K rows/sec per core. We hope that our findings can shed light on the design and implementation of other fast data analytics systems in the future.en_US
dc.description.sponsorshipEuropean Union ; TÜBİTAK ; IBM Shared University Research program ; Turkish Telecomm ; Avea Labs
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.relationinfo:eu-repo/grantAgreement/EC/FP7/256537en_US
dc.relationinfo:turkey/grantAgreement/TUBITAK/190E194en_US
dc.relation.ispartofBig Data (BigData Congress), 2013 IEEE International Congress on
dc.rightsrestrictedAccess
dc.titleOnline association rule mining over fast dataen_US
dc.typeConference paperen_US
dc.peerreviewedyesen_US
dc.publicationstatuspublisheden_US
dc.contributor.departmentÖzyeğin University
dc.contributor.authorID(ORCID 0000-0002-6159-0484 & YÖK ID 43541) Arı, İsmail
dc.contributor.ozuauthorArı, İsmail
dc.identifier.startpage110
dc.identifier.endpage117
dc.identifier.wosWOS:000332528300015
dc.identifier.doi10.1109/BigData.Congress.2013.77
dc.subject.keywordsData miningen_US
dc.subject.keywordsMusicen_US
dc.subject.keywordsSocial networking (online)en_US
dc.identifier.scopusSCOPUS:2-s2.0-84885993411
dc.contributor.ozugradstudentÖlmezoğulları, Erdi
dc.contributor.authorMale2
dc.relation.publicationcategoryConference Paper - International - Institutional Academic Staff and Graduate Student


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