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Titlebook: Kunden-werben-Kunden-Kampagnen; Eine empirische Anal Daniel Nießing Book 2007 Gabler Verlag | Springer Fachmedien Wiesbaden GmbH, Wiesbaden

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t corrective action. The core of our approach is an impact model language equipped with a formal semantics defined in terms of Discrete Time Markov Chains. To validate our approach, we show how employing our language can improve the accuracy of predictions used for decision-making in the Rainbow fra
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s. Currently, . can decide bounded-phase reachability problem for three types of channel semantics, namely lossy, stuttering and unordered channels. Messages inside these channels can be lost, duplicated and re-arranged, respectively. . efficiently and uniformly reduces the bounded-phase reachabilit
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s. Currently, . can decide bounded-phase reachability problem for three types of channel semantics, namely lossy, stuttering and unordered channels. Messages inside these channels can be lost, duplicated and re-arranged, respectively. . efficiently and uniformly reduces the bounded-phase reachabilit
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e property. A unique feature of our approach is that the generated assumptions are UML state machines. Moreover, our Teacher works at the UML level: all queries from the learning algorithm are answered by generating and verifying behavioral UML systems.
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