Towards Burst Detection for Non-Stationary Stream Data

Detecting bursts in data streams is an important and challenging task, especially in stock market, traffic control or sensor network streams. Burst detection means the identification of non regular behavior within data streams. A specifically crucial challenge on burst detection is to identify bursts in the case of non-stationary data. One approach is to apply thresholds to discover such bursts. In this paper, we propose a new approach to dynamically identify suitable thresholds using techniques known from time series forecasting. We present fundamentals and discuss requirements for threshold-based burst detection on stream data containing arbitrary trends and periods.

• Type: Conference
• Year: 2008
• Where: LWA
• Authors: Daniel Klan, Marcel Karnstedt, Christian Pölitz, Kai-Uwe Sattler
• Keywords: Burst Detection, Data Streams, LWA 2008, Sensor Network Streams, Software Engineering