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RNTI

MODULAD
Detecting Anomalies in Data Streams using Statecharts
In EGC 2010, vol. RNTI-E-19, pp.635-636
Résumé
The environment around us is progressively equipped with
various sensors, producing data continuously. The applications using
these data face many challenges, such as data stream integration over an
attribute (such as time) and knowledge extraction from raw data. In this
paper we propose one approach to face those two challenges. First, data
streams integration is performed using statecharts which represents a
resume of data produced by the corresponding data producer. Second,
we detect anomalous events over temporal relations among statecharts.
We describe our approach in a demonstration scenario, that is using a
visual tool called Patternator