Finding periodic outliers over a monogenetic event stream

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 Δt, derived from delta-time mining, or SUP(t, t+Δt). We provide efficient algorithms for finding the highest support Δt on a small and resource-limited sensor device. Our experiments compare memory efficiency and accuracy, on a variety of event patterns, monogenesis, polygenesis, and semi-random.