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Matching Business Process Workflows across Abstraction Levels

机译:匹配跨抽象级别的业务流程工作流程

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In Business Process Modeling, several models are defined for the same system, supporting the transition from business requirements to IT implementations. Each of these models targets a different abstraction level and stakeholder perspective. In order to maintain consistency among these models, which has become a major challenge not only in this field, the correspondence between them has to be identified. A correspondence between process models establishes which activities in one model correspond to which activities in another model. This paper presents an algorithm for determining such correspondences. The algorithm is based on an empirical study of process models at a large company in the banking sector, which revealed frequent correspondence patterns between models spanning multiple abstraction levels. The algorithm has two phases, first establishing correspondences based on similarity of model element attributes such as types and names and then refining the result based on the structure of the models. Compared to previous work, our algorithm can recover complex correspondences relating whole process fragments rather than just individual activities. We evaluate the algorithm on 26 pairs of business-technical and technical-IT level models from four real-world projects, achieving overall precision of 93% and recall of 70%. Given the substantial recall and the high precision, the algorithm helps automating significant part of the correspondence recovery for such models.
机译:在业务流程建模中,为同一系统定义了多种型号,支持从业务需求转换到IT实现。这些模型中的每一个都针对不同的抽象级别和利益相关者的观点。为了保持这些模型之间的一致性,这已经成为不仅在该领域的主要挑战中,必须识别它们之间的对应关系。过程模型之间的对应关系建立了一个模型中的哪些活动对应于另一模型中的哪些活动。本文介绍了一种确定这种对应关系的算法。该算法基于银行业一家大公司的过程模型的实证研究,揭示了跨越多个抽象级别的模型之间的频繁对应模式。该算法具有两个阶段,首先基于模型元素属性的相似性建立对应关系,例如类型和名称,然后基于模型的结构改进结果。与以前的工作相比,我们的算法可以恢复与整个过程碎片而不是单个活动的复杂对应关系。我们评估了来自四个现实项目的26对业务技术和技术 - 技术IT级模型的算法,实现了93%的总体精度,召回了70%。鉴于大量召回和高精度,该算法有助于为这些模型自动化对应恢复的重要部分。

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