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Graph-based Process Discovery containing Invisible Non-Prime Task in Procurement of Animal-Based Ingredient of Halal Restaurants

机译:基于图的过程发现,在清真餐馆的基于动物成分的采购中,包含不可见的非素数任务

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A process model based on animal-based ingredients' running procurement will help halal level examiners check halal implementation based on Halal Critical Control Points (HCCP). Process discovery is a study for automatically forming a process model based on a log of running processes. There are several algorithms of process discovery, such as Alpha++ and Alpha#. However, the procurement of animal-based ingredient processes has an invisible non-prime task that has not been discussed in the existing algorithms. This research proposes a graph-based process discovery algorithm to form a process model containing invisible non-prime tasks based on the procurement processes. The log of procurement processes is obtained by using a business process management application, i.e., ProcessMaker. This research evaluates the proposed graph-based algorithm by comparing it with Alpha++ and Alpha# based on fitness and precision. The evaluation verifies that the proposed graph-based algorithm has a better quality of the obtained process model than Alpha++ and Alpha#. The fitness and precision of the graph-based algorithm are 1 and 1. On the other hand, the precisions of Alpha++ and Alpha# are 0.43 and 0.43, respectively.
机译:一种基于动物成分的运行采购的过程模型将有助于清真级审查员根据清真临界控制点(HCCP)检查清真实施。过程发现是基于运行进程的日志自动形成过程模型的研究。有几种过程发现的算法,如alpha ++和alpha#。然而,动物的成分过程的采购具有尚未在现有算法中讨论的不可见的非素数任务。该研究提出了一种基于图形的过程发现算法,用于基于采购过程形成包含不可见的非Prime任务的过程模型。通过使用业务流程管理应用程序,即ProcessMaker获取采购流程的日志。本研究通过基于健身和精度将其与Alpha ++和Alpha#进行比较来评估所提出的基于图形的算法。评估验证所提出的基于图形的算法具有比Alpha ++和Alpha#的获得的过程模型更好的质量。另一方面,基于图形的算法的适应性和精度为1和1.,α++和alpha#的精确分别为0.43和0.43。

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