Semantics of Higraphs for Process Modeling and Analysis

E. Kushnareva, I. Rychkova, B. L. Grand
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引用次数: 1

Abstract

Knowledge and experience of a case manager re- mains a key success factor for Case Management Processes (CMPs). When a number of influential parameters is high, a number of possible scenarios grows significantly. Automated guidance in scenario evaluation and activity planning would be of a great help. In our previous work, we defined the statecharts semantics for visualisation and simulation of CMP scenarios. In this work, we formalise the state- oriented models with higraphs: higraphs provide mathematical foundation for statecharts and eventually enable a wide panoply of algorithms for process analysis and optimisation. We show how a statecharts diagram can be transformed into higraph and analysed at run-time with graph algorithms. In particular, we take an example of the Shortest Path algorithm and show how this algorithm can be used in order to guide the case manager suggesting her the best process scenario. Compared to BPM approaches, a state-oriented process scenario does not specify concrete activities but only the objectives and constraints to be met. Thus, our approach does not prescribe but describe an activity to be executed next. The manager can define an activity that fit the description "on the fly", based on her experience and intuition.
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用于过程建模和分析的图的语义
案例管理人员的知识和经验仍然是案例管理流程(cmp)成功的关键因素。当一些有影响的参数很高时,一些可能的情况会显著增加。在情景评估和活动规划中进行自动化指导将会有很大的帮助。在我们之前的工作中,我们定义了用于可视化和模拟CMP场景的状态图语义。在这项工作中,我们用图表形式化了面向状态的模型:图表为状态图提供了数学基础,并最终为过程分析和优化提供了广泛的算法。我们展示了如何将状态图转换为图形,并在运行时使用图形算法进行分析。特别地,我们将以最短路径算法为例,并展示如何使用该算法,以指导案例管理人员向她推荐最佳流程场景。与BPM方法相比,面向状态的流程场景不指定具体的活动,而只指定要满足的目标和约束。因此,我们的方法不是规定而是描述接下来要执行的活动。经理可以根据自己的经验和直觉,定义符合“动态”描述的活动。
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