Achievement

Kang, J. M. et al, Discovering Flow Anomalies on a Smarter Water Infrastructure System, Data Mining for Smarter Infrastructure Workshop, In SIAM Intl. Conf. on Data Mining, 2010.

Flow anomaly discovery identifies dominant time intervals where the fraction of time instants of significantly mis-matched sensor readings exceed the given percentage-threshold. It is an important problem in environmental flow monitoring networks. However, it is computationally expensive because of the large number of time instants of measurement and potentially long delays between consecutive sensors. Computation overhead is brought down significantly by restricting the start and end points of a window to coincide with transient FAs, using a smart counter and efficient pruning techniques. Evaluation using a real dataset shows our proposed approach outperforms naive alternatives.