Scalable Proactive Event-Driven Decision Making

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Alexander Artikis, Chris Baber, Pedro Bizarro, Carlos Canudas de Wit, Opher Etzion, Fabiana Fournier, Paul Goulart, Andrew Howes, John Lygeros, Georgios Paliouras, Assaf Schuster, Izchak Sharfman
This paper proposes a methodology for proactive event-driven decision making. Proper decisions are made by forecasting events prior to their occurrence. Motivation for proactive decision making stems from social and economic factors, and is based on the fact that prevention is often more effective than the cure. The decisions are made in real time and require swift and immediate processing of Big Data, that is, extremely large amounts of noisy data flooding in from various locations, as well as historical data. The methodology will recognize and forecast opportunities and threats, making the decision to capitalize on the opportunities and mitigate the threats. This will be explained through user-interaction and the decisions of human operators, in order to ultimately facilitate proactive decision making.
Software and Knowledge Engineering Laboratory (SKEL)
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Technology and Society Magazine, IEEE
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