Online association rule mining over fast data
dc.contributor.author | Ölmezoğulları, Erdi | |
dc.contributor.author | Arı, İsmail | |
dc.date.accessioned | 2014-11-24T08:02:48Z | |
dc.date.available | 2014-11-24T08:02:48Z | |
dc.date.issued | 2013 | |
dc.identifier.isbn | 978-0-7695-5006-0 | |
dc.identifier.uri | http://hdl.handle.net/10679/661 | |
dc.identifier.uri | http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6597126&tag=1 | |
dc.description | Due to copyright restrictions, the access to the full text of this article is only available via subscription. | en_US |
dc.description.abstract | To 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.sponsorship | European Union ; TÜBİTAK ; IBM Shared University Research program ; Turkish Telecomm ; Avea Labs | |
dc.language.iso | eng | en_US |
dc.publisher | IEEE | en_US |
dc.relation | info:eu-repo/grantAgreement/EC/FP7/256537 | en_US |
dc.relation | info:turkey/grantAgreement/TUBITAK/190E194 | en_US |
dc.relation.ispartof | Big Data (BigData Congress), 2013 IEEE International Congress on | |
dc.rights | restrictedAccess | |
dc.title | Online association rule mining over fast data | en_US |
dc.type | Conference paper | en_US |
dc.peerreviewed | yes | en_US |
dc.publicationstatus | published | en_US |
dc.contributor.department | Özyeğin University | |
dc.contributor.authorID | (ORCID 0000-0002-6159-0484 & YÖK ID 43541) Arı, İsmail | |
dc.contributor.ozuauthor | Arı, İsmail | |
dc.identifier.startpage | 110 | |
dc.identifier.endpage | 117 | |
dc.identifier.wos | WOS:000332528300015 | |
dc.identifier.doi | 10.1109/BigData.Congress.2013.77 | |
dc.subject.keywords | Data mining | en_US |
dc.subject.keywords | Music | en_US |
dc.subject.keywords | Social networking (online) | en_US |
dc.identifier.scopus | SCOPUS:2-s2.0-84885993411 | |
dc.contributor.ozugradstudent | Ölmezoğulları, Erdi | |
dc.contributor.authorMale | 2 | |
dc.relation.publicationcategory | Conference Paper - International - Institutional Academic Staff and Graduate Student |
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