TELKO analizleri için ölçeklenebilir akan profil kümelemesi

Many telco analytics require maintaining call profiles based on recent customer call patterns. Such profiles are typically organized as aggregations computed at different time scales over the recent customer interactions. Clustering these profiles is needed to group customers with similar calling patterns and to build aggregate models for them. Example applications include optimizing tarifs, segmentation, and usage forecasting. In this thesis, we present an approach for clustering profiles that are incrementally maintained over a stream of updates. 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 centralized node, and use a greedy algorithm with novel affinity 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.