Adaptive burst detection in a stream engine

Authors:
Marcel Karnstedt;Daniel Klan;Christian Pölitz;Kai-Uwe Sattler;Conny Franke
Affiliations:
Ilmenau University of Technology, Germany;Ilmenau University of Technology, Germany;Ilmenau University of Technology, Germany;Ilmenau University of Technology, Germany;University of California, Davis
Venue:
Proceedings of the 2009 ACM symposium on Applied Computing
Year:
2009

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Abstract

Detecting bursts in data streams is an important and challenging task. Due to the complexity of this task, usually burst detection cannot be formulated using standard query operators. Therefore, we show how to integrate burst detection for stationary as well as non-stationary data into query formulation and processing, from the language level to the operator level. Afterwards, we present fundamentals of threshold-based burst detection. We focus on the applicability of time series forecasting techniques in order to dynamically identify suitable thresholds for stream data containing arbitrary trends and periods. The proposed approach is evaluated with respect to quality and performance on synthetic and real-world sensor data using a full-fledged DSMS.