Sensor stream pattern mining for automatic anomaly recognition and intervention

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This project will develop a general framework of accurate automatic recognition of meaningful anomalies in multivariate sensor data streams that require action to avoid detrimental events and allow automatic intervention for efficient mitigation. Existing anomaly recognition algorithms miss many patterns and manually relating co-occurring stream patterns to an anomaly is inefficient and error-prone. The project expects to develop methods for intercepting a combination of co-occurring patterns to ascertain what an anomaly is and identify the anomaly and its stages that indicate the necessity of intervention. This project will advance techniques for sensor stream data mining and enable general applications of sensor surveillance and automatic mechanical intervention.