We have been developing formal computational methods which take as input streams of low-level events, e.g. sensor-based events, such as a change in temperature, and combine them to infer complex high-level events of interest, such as the start of a fire incident or a fault in the cooling system of a vehicle. Our methods support real-time event recognition over high-velocity data streams, online structure learning for accurate event recognition over noisy relational streams, as well as complex event forecasting for proactive decision-making. An overview of our research activities may be found in this paper and this presentation, while latest news, running projects and other information can be found in our webpage.

Some applications in which we have applied our methods:

• Maritime Situational Awareness: Analyze vessel position streams to detect in real-time dangerous, suspicious and illegal activities.
• Fleet Management: Analyze vehicle position streams for real-time fleet management.
• Activity Recognition: Analyze camera feeds for real-time assisted daily living.
• Forecasting in cancer cell simulations: Forecast the outcome of cancer cell simulations, in order to optimise the use of resources on which they run.