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Activity prediction in process mining using the WoMan framework

机译:使用WoMan框架进行流程挖掘中的活动预测

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Process Management techniques are useful in domains where the availability of a (formal) process model can be leveraged to monitor, supervise, and control a production process. While their classical application is in the business and industrial fields, other domains may profitably exploit Process Management techniques. Some of these domains (e.g., people's behavior, General Game Playing) are much more flexible and variable than classical ones, and, thus, raise the problem of predicting which activities will be carried out next, a problem that is not so compelling in classical fields. When the process model is learned automatically from examples of process executions, which is the task of Process Mining, the prediction performance may also provide indirect indications on the correctness and reliability of the learned model. This paper proposes and compares two strategies for activity prediction using the WoMan framework for workflow management. The former proved to be able to handle complex processes, the latter is based on the classic and consolidated Naive Bayes approach. An experimental validation allows us to draw considerations on the pros and cons of each, used both in isolation and in combination.
机译:流程管理技术在可以利用(正式)流程模型的可用性来监视,监督和控制生产流程的领域中非常有用。尽管它们的经典应用是在商业和工业领域,但其他领域也可以从过程管理技术中获利。这些领域中的某些领域(例如,人们的行为,一般的游戏玩法)比经典领域更具灵活性和可变性,因此,出现了预测接下来将进行哪些活动的问题,这在经典领域并没有那么引人注目。领域。当从流程执行的示例中自动学习流程模型时(流程挖掘的任务),预测性能还可以间接指示所学习模型的正确性和可靠性。本文提出并比较了使用WoMan框架进行工作流管理的活动预测策略。事实证明,前者能够处理复杂的流程,而后者则基于经典的合并Naive Bayes方法。通过实验验证,我们可以对每种方法的优缺点进行考虑,可以单独使用也可以组合使用。

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