Columbia is one of the co-hosts, through our center for Computing Systems for Data-Driven Science.
Speaker: Dr. Martha Symko-Davies, Laboratory Program Manager, Energy Systems Integration (ESI), National Renewable Energy Laboratory (NREL)
Data Science Conference & Expo
Accelerate your data science knowledge, training, and network.
All in one event.
Data-empowered algorithms filter and amplify our choices and are thereby increasingly shaping our professional, political, and personal realities.
Jeannette M. Wing is Avanessians Director of the Data Science Institute and Professor of Computer Science at Columbia University.
Data for Good: Data Science at Columbia
Simple Rules for Decision-Making
The Data Science Institute (DSI) at Columbia University and Bloomberg are pleased to announce a workshop on "Machine Learning in Finance".
Graph based event detection in streams: the twitter case.
Due to its instantaneous nature, Twitter has been established as a major communication medium. Among others, people use the service to report latest news and to comment about real-world events. Users show particular interest in social events such as large parties, political campaigns and sporting events but also for emergency events such as natural disasters and terrorist attacks. Automated and real-time event detection in this case is an interesting challenge. We present our work on this topic capitalizing on modeling the stream as an evolving graph of words and then based on its evolution patterns detecting events. To identify important moments the system detects rapid changes in the graphs’ edge weights using a convex optimization formulation. Then we need to summarize the event in the best feasible way. We present a method that generates real-time summaries of events using only posts collected from Twitter. The system then extracts a few tweets that best describe the chain of interesting occurrences in the event using a greedy algorithm that maximizes a non-decreasing sub-modular function. Through extensive experiments on real-world sporting events, we show that the proposed system can effectively capture the sub-events, and that it clearly outperforms the dominant sub-event detection method.