A Data-Warehouse/OLAP Framework for Scalable Telecommunication Tandem Traffic Analysis

In a telecommunication network, hundreds of millions of call detail records (CDRs) are generated daily. Applications such as tandem traffic analysis require the collection and mining of CDRs on a continuous basis. The data volumes and data flow rates pose serious scalability and performance challenges. This has motivated us to develop a scalable datawarehouse/OLAP framework, and based on this framework, tackle the issue of scaling the whole operation chain, including data cleansing, loading, maintenance, access and analysis. We introduce the notion of dynamic data warehousing for managing information at different aggregation levels with different life spans. We use OLAP servers, together with the associated multidimensional databases, as a computation platform for data caching, reduction and aggregation, in addition to data analysis. The framework supports parallel computation for scaling up data mining, and supports incremental OLAP for providing continuous data mining. A tandem traffic analysis engine is implemented on the proposed framework. In addition to the parallel and incremental computation architecture, we provide a set of application-specific optimization mechanisms for scaling performance. These mechanisms fit well into the above framework. Our experience demonstrates the practical value of the above framework in supporting an important class of telecommunication business intelligence applications.