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Graph theory and model collection management: conceptual framework and runtime analysis of selected graph algorithms

机译:图论和模型集合管理:选定图算法的概念框架和运行时分析

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摘要

Analysing conceptual models is a frequent task of business process management (BPM), for instance to support comparison or integration of business processes, to check business processes for compliance or weaknesses, or to tailor conceptual models for different audiences. As recently, many companies have started to maintain large model collections and analysing such collections manually may be laborious, practitioners have articulated a demand for automatic model analysis support. Hence, BPM scholars have proposed a plethora of different model analysis techniques. As virtually any conceptual model can be interpreted as a mathematical graph and model analysis techniques often include some kind of graph problem, in this paper, we introduce a graph algorithm based model analysis framework that can be accessed by specialized model analysis techniques. To prove that basic graph algorithms are feasible to support such a framework, we conduct a performance analysis of selected graph algorithms.
机译:分析概念模型是业务流程管理(BPM)的一项常见任务,例如,支持业务流程的比较或集成,检查业务流程的合规性或弱点或为不同的受众量身定制概念模型。近来,许多公司已开始维护大型模型集合,并且手动分析此类集合可能很费力,从业人员提出了对自动模型分析支持的需求。因此,BPM学者提出了许多不同的模型分析技术。由于实际上任何概念模型都可以解释为数学图形,并且模型分析技术通常包含某种图形问题,因此在本文中,我们介绍了一种基于图形算法的模型分析框架,可以通过专门的模型分析技术进行访问。为了证明基本的图算法对于支持这种框架是可行的,我们对选定的图算法进行了性能分析。

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