Aggregate profile clustering for telco analytics

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dc.citation.epage1237en_US
dc.citation.issueNumber12en_US
dc.citation.spage1234en_US
dc.citation.volumeNumber6en_US
dc.contributor.authorAbbasoğlu, M.A.en_US
dc.contributor.authorGedik, B.en_US
dc.contributor.authorFerhatosmanoğlu H.en_US
dc.date.accessioned2016-02-08T09:36:56Z
dc.date.available2016-02-08T09:36:56Z
dc.date.issued2013en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.description.abstractMany telco analytics require maintaining call profiles based on recent customer call patterns. Such call profiles are typically organized as aggregations computed at different time scales over the recent customer interactions. Customer call profiles are key inputs for analytics targeted at improving operations, marketing, and sales of telco providers. Many of these analytics require clustering customer call profiles, so that customers with similar calling patterns can be modeled as a group. Example applications include optimizing tariffs, customer segmentation, and usage forecasting. In this demo, we present our system for scalable aggregate profile clustering in a streaming setting. We focus on managing anonymized segments of customers for tariff optimization. Due to the large number of customers, maintaining profile 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 profiles 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 profiles to machines should be continuously revised, yet one should minimize the migration of profiles 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 centralize node, and use a greedy algorithm with novel affinity heuristics to revise the partitioning. We present a demo that showcases our Storm and Hbase based implementation of the proposed solution in the context of a customer segmentation application. © 2013 VLDB Endowment.en_US
dc.identifier.issn21508097
dc.identifier.urihttp://hdl.handle.net/11693/20873
dc.language.isoEnglishen_US
dc.source.titleProceedings of the VLDB Endowmenten_US
dc.subjectClustering accuracyen_US
dc.subjectClustering customersen_US
dc.subjectCustomer interactionen_US
dc.subjectCustomer segmentationen_US
dc.subjectDifferent time scaleen_US
dc.subjectDistributed stream processingen_US
dc.subjectGreedy algorithmsen_US
dc.subjectOnline processingen_US
dc.subjectAggregatesen_US
dc.subjectDistributed parameter control systemsen_US
dc.subjectOptimizationen_US
dc.subjectSalesen_US
dc.titleAggregate profile clustering for telco analyticsen_US
dc.typeArticleen_US

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