Clustering Events on Streams Using Complex Context Information

Monitoring applications play an increasingly important role in many domains. They detect events in monitored systems and take actions such as invoke a program or notify an administrator. Often administrators must then manually investigate events to figure out the source of a problem. Stream processing engines (SPEs) are general purpose data management systems for monitoring applications. They provide low-latency stream processing but have limited or no support for manual event investigation. In this paper, we propose a new technique for an SPE to support event investigation by automatically classifying events on streams. Unlike previous stream clustering algorithms, our approach takes into account complex user-defined contexts for events. Our approach comprises three key components: an event context data model, a distance measure for event contexts, and an online clustering algorithm for event contexts. We evaluate our approach using synthetic data and show that complex context info...

• Type: Conference
• Year: 2008
• Where: ICDM
• Authors: YongChul Kwon, Wing Yee Lee, Magdalena Balazinska, Guiping Xu
• Keywords: Data Mining, Event Contexts, Event Investigation, ICDM 2008, Stream Processing