Tutorial in IEEE International Conference on Big Data 2018 

Big Data Analytics on Societal Event Forecasting

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Abstract

Spatio-temporal societal event forecasting, which has traditionally been prohibitively challenging, is now becoming possible and experiencing rapid growth thanks to the big data from Open Source Indicators (OSI) such as social media, news sources, blogs, economic indicators, and other meta-data source. Spatio-temporal societal event forecasting benefits the society in various aspects, such as political crises, humanitarian crises, mass violence, riots, mass migrations, disease outbreaks, economic instability, resource shortages, responses to natural disasters, and others.

Different from traditional event detection that identifies ongoing events, event forecasting focuses on predicting the future events yet to happen. Also different from traditional spatio-temporal prediction on numerical indices, spatio-temporal event forecasting needs to leverage the heterogeneous information from OSI to discover the predictive indicators and mappings to future societal events. The resulting problems typically require the predictive modeling techniques that can jointly handle semantic, temporal, and spatial information, and require a design of efficient algorithms that scale to high-dimensional large real-world datasets.

Taxonomy of Research Works

References

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