EventMapper: Detecting Real-World Physical Events Using Corroborative
and Probabilistic Sources
- URL: http://arxiv.org/abs/2001.08700v1
- Date: Thu, 23 Jan 2020 17:47:31 GMT
- Title: EventMapper: Detecting Real-World Physical Events Using Corroborative
and Probabilistic Sources
- Authors: Abhijit Suprem and Calton Pu
- Abstract summary: EventMapper is a framework to support event recognition of small yet equally costly events.
It integrates high-latency, high-accuracy corroborative sources such as physical sensors with low-latency, noisy probabilistic sources such as social media streams.
We describe three applications built on EventMapper for landslide, wildfire, and flooding detection.
- Score: 3.210653757360955
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The ubiquity of social media makes it a rich source for physical event
detection, such as disasters, and as a potential resource for crisis management
resource allocation. There have been some recent works on leveraging social
media sources for retrospective, after-the-fact event detection of large events
such as earthquakes or hurricanes. Similarly, there is a long history of using
traditional physical sensors such as climate satellites to perform regional
event detection. However, combining social media with corroborative physical
sensors for real-time, accurate, and global physical detection has remained
unexplored.
This paper presents EventMapper, a framework to support event recognition of
small yet equally costly events (landslides, flooding, wildfires). EventMapper
integrates high-latency, high-accuracy corroborative sources such as physical
sensors with low-latency, noisy probabilistic sources such as social media
streams to deliver real-time, global event recognition. Furthermore,
EventMapper is resilient to the concept drift phenomenon, where machine
learning models require continuous fine-tuning to maintain high performance.
By exploiting the common features of probabilistic and corroborative sources,
EventMapper automates machine learning model updates, maintenance, and
fine-tuning. We describe three applications built on EventMapper for landslide,
wildfire, and flooding detection.
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