Project Summary:

Streaming data analytics in large sensor networks is increasingly important due to both the volume of data collected and the real-time decision making nature. Cyber-physical systems, social networks/social media, smart grids, smart transport systems are just few examples of large, distributed sensor networks that would benefit from real-time streaming data analytics for achieving situation awareness.

This project continues the existing work developed in KDE on graph-based data mining for both historic data and online streaming data. The primary areas to investigate are anomaly detection and concept drifting in a single sensor data stream and across multiple sensor data streams. Anomalies represent unusual situations that may need further investigation whilst concept drifting research is about how to discover gradual situation changes that occur over time, which may appear as anomalies in the first instance.

Contact Details:

Weiru Liu


Telephone:+44 (0)28 9097 4896