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dc.contributor.authorÇelebi, Ö. F.
dc.contributor.authorZeydan, E.
dc.contributor.authorArı, İsmail
dc.contributor.authorİleri, Ö.
dc.contributor.authorErgüt, S.
dc.description.abstractMost mobile telecommunication operators receive an overwhelming number of alarms in their networks. Network support specialists are faced with the challenge of picking the most important alarms in advance that can cause severe damages to the system or disrupt the service. A system that can discover alarm correlations and alarm rules then notify network administrators can significantly increase the efficiency of Network Operation Centers (NOC) of these mobile operators. This paper provides a new alarm correlation, rule discovery, and significant rule selection technique based on analysis of real data collected from a mobile telecom operator. We present a method based on sequential rule mining algorithm with an additional parameter called time-confidence. The time-confident rules found by this method are processed more efficiently in real-time Complex Event Processing (CEP) systems that require exact time-window values during monitoring. Furthermore, compared to traditional sequential rule mining, our proposed method adds another support dimension to eliminate meaningless rules that appear due to wrong settings of minimum support-confidence thresholds with respect to the nature of data.
dc.description.sponsorshipAveaLabs ; TÜBİTAK
dc.relation.ispartofIEEE International Conference on Data Mining (ICDM)
dc.titleAlarm sequence rule mining extended with a time confidence parameteren_US
dc.typeConference paperen_US
dc.contributor.departmentÖzyeğin University
dc.contributor.ozuauthorArı, İsmail
dc.subject.keywordsTime confidence
dc.subject.keywordsAlarm correlation
dc.subject.keywordsSequential rule mining
dc.subject.keywordsComplex event processing
dc.subject.keywordsTelecom network operation

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