Scalable streaming profile clustering for telco analytics

Many 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.