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International Workshop on Ubiquitous Data Management
Finding Periodic Outliers over a Monogenetic Event Stream
Tokyo, Japan
April 04-April 04
ISBN: 0-7695-2411-7
Kimio Kuramitsu, Yokohama National University 79-1 Tokiwadai, Hodogayaku, Yokohama 240-8501 JAPAN

Sensors are active everywhere. Enormous volumes of sensed events are sent over the data streams, while most of applications want to focus on events that would be curious. We propose a technique for mining periodicities and predicting its outliers from the stream. The key to our technique is a simple periodic pattern {\Delta x}t, derived from delta-time mining, or SUP(t, t+{\Delta x}t). We provide efficient algorithms for finding the highest support {\Delta x}t on a small and resource-limited sensor device. Our experiments will compare memory efficiency and accuracy, on a variety of event patterns, monogenesis, polygenesis, and semi-random.

Citation:
Kimio Kuramitsu, "Finding Periodic Outliers over a Monogenetic Event Stream," udm, pp.97-104, International Workshop on Ubiquitous Data Management, 2005
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