Communications of the ACM
The Power of Events: An Introduction to Complex Event Processing in Distributed Enterprise Systems
The Power of Events: An Introduction to Complex Event Processing in Distributed Enterprise Systems
Comparing autonomic and proactive computing
IBM Systems Journal
Inferring Internet denial-of-service activity
ACM Transactions on Computer Systems (TOCS)
Inference of Security Hazards from Event Composition Based on Incomplete or Uncertain Information
IEEE Transactions on Knowledge and Data Engineering
Event-processing network model and implementation
IBM Systems Journal
Quantifying event correlations for proactive failure management in networked computing systems
Journal of Parallel and Distributed Computing
Event Processing in Action
Towards proactive event-driven computing
Proceedings of the 5th ACM international conference on Distributed event-based system
Proactive Business Process Compliance Monitoring with Event-Based Systems
EDOCW '11 Proceedings of the 2011 IEEE 15th International Enterprise Distributed Object Computing Conference Workshops
Efficient Processing of Uncertain Events in Rule-Based Systems
IEEE Transactions on Knowledge and Data Engineering
CDMW 2012 - city data management workshop: workshop summary
Proceedings of the 21st ACM international conference on Information and knowledge management
MigCEP: operator migration for mobility driven distributed complex event processing
Proceedings of the 7th ACM international conference on Distributed event-based systems
Proceedings of the 7th ACM international conference on Distributed event-based systems
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During the movie "Source Code" there is a shift in the plot; from (initially) reacting to a train explosion that already occurred and trying to eliminate further explosions, to (later) changing the reality to avoid the original train explosion. Whereas changing the history after events have happened is still within the science fiction domain, changing the reality to avoid events that have not happened yet is, in many cases, feasible, and may yield significant benefits. We use the term proactive behavior to designate the change of what will be reality in the future. In particular, we focus on proactive event-driven computing: the use of event-driven systems to predict future events and react to them before they occur. In this paper we start our investigation of this large area by constructing a model and end-to-end implementation of a restricted subset of basic proactive applications that is trying to eliminate a single forecasted event, selecting between a finite and relatively small set of feasible actions, known at design time, based on quantified cost functions over time. After laying out the model, we describe the extensions required of the conceptual architecture of event processing to support such applications: supporting proactive agents as part of the model, supporting the derivation of forecasted events, and supporting various aspects of uncertainty; next, we show a decision algorithm that selects among the alternatives. We demonstrate the approach by implementing an example of a basic proactive application in the area of condition based maintenance, and showing experimental results.