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dc.contributor.advisorGüdükbay, Uğur
dc.contributor.authorAbbasoğlu, Mehmet Ali
dc.date.accessioned2016-01-08T18:25:38Z
dc.date.available2016-01-08T18:25:38Z
dc.date.issued2013
dc.identifier.urihttp://hdl.handle.net/11693/15857
dc.descriptionAnkara : The Department of Computer Engineering and the Graduate School of Engineering and Science of Bilkent University, 2013.en_US
dc.descriptionThesis (Master's) -- Bilkent University, 2013.en_US
dc.descriptionIncludes bibliographical references leaves 42-44.en_US
dc.description.abstractMany telco analytics require maintaining call pro les based on recent customer call patterns. Such pro les are typically organized as aggregations computed at di erent time scales over the recent customer interactions. Clustering these pro les is needed to group customers with similar calling patterns and to build aggregate models for them. Example applications include optimizing tari s, segmentation, and usage forecasting. In this thesis, we present an approach for clustering pro les that are incrementally maintained over a stream of updates. Due to the large number of customers, maintaining pro le clusters have high processing and memory resource requirements. In order to tackle this problem, we apply distributed stream processing. However, in the presence of distributed state, it is a major challenge to partition the pro les over machines (nodes) such that memory and computation balance is maintained, while keeping the clustering accuracy high. Furthermore, to adapt to potentially changing customer calling patterns, the partitioning of pro les to machines should be continuously revised, yet one should minimize the migration of pro les so as not to disturb the online processing of updates. We provide a re-partitioning technique that achieves all these goals. We keep micro-cluster summaries at each node, collect these summaries at a centralized node, and use a greedy algorithm with novel a nity heuristics to revise the partitioning. We present a demo application that showcases our Storm and Hbase based implementation in the context of a customer segmentation application.en_US
dc.description.statementofresponsibilityAbbasoğlu, Mehmet Alien_US
dc.format.extentx, 44 leaves, graphics, illustrationsen_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDistributed clusteringen_US
dc.subjectAggregate pro le clusteringen_US
dc.subjectTelcoen_US
dc.subject.lccQA278 .A33 2013en_US
dc.subject.lcshCluster analysis--Data processing.en_US
dc.subject.lcshDocument clustering.en_US
dc.subject.lcshInformation retrieval.en_US
dc.subject.lcshTelecommunication.en_US
dc.titleScalable streaming profile clustering for telco analyticsen_US
dc.typeThesisen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US
dc.identifier.itemidB139545


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