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A basic model for proactive event-driven computing

Published: 16 July 2012 Publication History

Abstract

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.

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cover image ACM Conferences
DEBS '12: Proceedings of the 6th ACM International Conference on Distributed Event-Based Systems
July 2012
410 pages
ISBN:9781450313155
DOI:10.1145/2335484
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 16 July 2012

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Author Tags

  1. decision making
  2. event processing
  3. proactive computing

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  • (2023)Predicting Accident Outcomes in Cross-Border Pipeline Construction Projects Using Machine Learning AlgorithmsArabian Journal for Science and Engineering10.1007/s13369-023-07964-w48:10(13771-13789)Online publication date: 17-Jun-2023
  • (2022)Conceptualizing Supply Chain Resilience: The Role of Complex IT InfrastructuresSystems10.3390/systems1002003510:2(35)Online publication date: 14-Mar-2022
  • (2022)Prescriptive Analytics: When Data- and Simulation-based Models Interact in a Cooperative Way2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)10.1109/SYNASC57785.2022.00009(1-8)Online publication date: Sep-2022
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