Real-time Event Detection for Emergency Response Tutorial
Alejandro Jaimes,Joel Tetreault
The amount of public data being generated on a daily basis has grown exponentially in the last few years and continues to increase at incredible speed. Most of this data is unstructured and includes text in different formats, in different languages, from many different sources; images, video, audio, and data from sensors. A lot of that data contains information about events happening all over the world, many of which require emergency response. Detecting events in public data, in real time, is therefore critical in many applications: from getting information to first responders as quickly as possible, to creating situational awareness in such emergency situations, as getting the right information to the right places as quickly as possible is critical in saving lives. When an event is ongoing, information on what is happening can be critical in making decisions to keep people safe and take control of the particular situation unfolding. First responders have to quickly make decisions that include what resources to deploy and where. Fortunately, in most emergencies, people use social media to publicly share information. At the same time, sensor data is increasingly becoming available. In order to do this, efficient computational approaches must detect and deliver the right information to the right destination. This tutorial will cover techniques at the state-of-the art to detect events in real-time from large-scale heterogeneous sources. We will focus on NLP, Computer Vision, and Anomaly Detection techniques. We will give specific examples and discuss relevant future research directions in Machine Learning, NLP, Computer Vision and other fields relevant to real time event detection. We will also discuss applications of event detection.
