SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
OLAP-Based Data Mining for Business Intelligence Applications in Telecommunications and E-commerce
DNIS '00 Proceedings of the International Workshop on Databases in Networked Information Systems
Visual Data Mining for Business Intelligence Applications
WAIM '00 Proceedings of the First International Conference on Web-Age Information Management
An OLAP-based Scalable Web Access Analysis Engine
DaWaK 2000 Proceedings of the Second International Conference on Data Warehousing and Knowledge Discovery
Proceedings of the 8th ACM international workshop on Data warehousing and OLAP
DADA: a data cube for dominant relationship analysis
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
ZELESSA: an enabler for real-time sensing, analysing and acting on continuous event streams
International Journal of Business Intelligence and Data Mining
SQL TVF Controlling Forms - Express Structured Parallel Data Intensive Computing
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
User Defined Partitioning - Group Data Based on Computation Model
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Traditional Business Intelligence vis-a-vis real-time Business Intelligence
International Journal of Information and Communication Technology
Update propagation in a streaming warehouse
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
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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 data-warehouse/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.