Advances in Knowledge Discovery and Data Mining

Modern communication networks generate large amounts of operational data, including traffic and utilization statistics and alarm/fault data at various levels of detail. These massive collections of network-management data can grow in the order of several Terabytes per year, and typically hide “knowledge” that is crucial to some of the key tasks involved in effectively managing a communication network (e.g., capacity planning and traffic engineering). In this short paper, we provide an overview of some of our recent and ongoing work in the context of the NEMESIS project at Bell Laboratories that aims to develop novel data warehousing and mining technology for the effective storage, exploration, and analysis of massive network-management data sets. We first give some highlights of our work on Model-Based Semantic Compression (MBSC), a novel data-compression framework that takes advantage of attribute semantics and data-mining models to perform lossy compression of massive network-data tables. We discuss the architecture and some of the key algorithms underlying SPARTAN , a model-based semantic compression system that exploits predictive data correlations and prescribed error tolerances for individual attributes to construct concise and accurate Classification and Regression Tree (CaRT) models for entire columns of a table. We also summarize some of our ongoing work on warehousing and analyzing network-fault data and discuss our vision of how data-mining techniques can be employed to help automate and improve faultmanagement in modern communication networks. More specifically, we describe the two key components of modern fault-management architectures, namely the event-correlation and the root-cause analysis engines, and propose the use of mining ideas for the automated inference and maintenance of the models that lie at the core of these components based on warehoused network data.