Extreme Events have recently become a topic of interest for the research community. It encompasses a wide spectrum of phenomena, including, among others, extreme weather events, pandemics, financial outbreaks, solar system explosions, mineral dam collapses, and social network attacks with fake news.
Due to the complexity of these events, non-linear and multi-phenomena, modeling through mathematical equations becomes very hard and, when possible, it requires a huge computing power to run the models in time for an efficient response from the authorities involved in responding to the events. In this context, data-driven models become a very interesting alternative. As data from different sources become available or have been collected for decades (as in the meteorology scenario) it enables learning from data strategies exploiting the current stage of data mining, machine learning, and deep learning models. However, integrating the huge amount of heterogeneous data needed to reproduce these phenomena requires efficient systems to store and process data.
Exploring the huge space of data management solutions currently available is paramount to come up with directions that the community may use to support their prediction applications. Moreover, once a data-driven approach is followed there is a need to formalize the problem. Logical formalisms and ontological approaches may be useful in precisely defining entities and their relationships involved in extreme events. This complex network of related information may be suitable for the construction of a knowledge graph for their representation. All these lines of research that have been developed by the data management community are candidates for supporting the research on extreme event detection. Conversely, we expect that new opportunities will arise with requirements for data management and analysis research. Thus, in this Workshop, we intend to foster the research in Brazil towards data-driven approaches to support event analytics, especially extreme event detection and prediction.