To prevent problems and capitalize on opportunities before they
even occur, we propose a methodology for proactive event-driven
decision-making. Decisions are triggered by forecasting events
instead of reacting to them once they happen. The motivation for
proactive computing 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 on-the-fly
processing of Big Data, that is, extremely large amounts of noisy
data flooding in from various locations, as well as historical data.
Proactive applications have been developed for several years [19].
Consider e.g. proactive security systems and proactive routing in
mobile ad-hoc networks. Proactive applications have been largely
developed in an ad hoc manner. In contrast, we aim to develop a
generic methodology for proactive event-driven computing.
To prevent problems and capitalize on opportunities before theyeven occur, we propose a methodology for proactive event-drivendecision-making. Decisions are triggered by forecasting eventsinstead of reacting to them once they happen. The motivation forproactive computing stems from social and economic factors, andis based on the fact that prevention is often more effective than thecure. The decisions are made in real-time and require on-the-flyprocessing of Big Data, that is, extremely large amounts of noisydata flooding in from various locations, as well as historical data.Proactive applications have been developed for several years [19].Consider e.g. proactive security systems and proactive routing inmobile ad-hoc networks. Proactive applications have been largelydeveloped in an ad hoc manner. In contrast, we aim to develop ageneric methodology for proactive event-driven computing.
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