Citation

Chua, Fang Fang and Chan, Gaik Yee and Yanuarifiani, Amarilis Putri (2019) Automating Business Process Model Generation from Ontology-based Requirements. ACM International Conference Proceeding Series, 147956. p. 205209. ISSN 2374-6769

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Abstract

Requirements elicitation process faces major challenges about how stakeholders can easily verify requirements. Requirements document allows developers to visualize requirements using modeling language to ensure stakeholders have the same perspective as them. It is also effective to give presentations to stakeholders about how business processes will be carried out after the requirements are implemented. Issues are raised in building requirements modeling as business users generally do not have enough knowledge to build requirements models in specific notations. Transforming requirements (natural language) into semi-formal notation (BPMN) manually lead to inconsistency of elements structure. The need to automatically generate requirements model become crucial because it will be the basis for the programming process. Existing studies are mostly concerned on auto-completion of modeling language using domain ontology as basic knowledge, and let the stakeholders building initial requirements model with limited knowledge. The idea of this paper is to propose a methodology for building business process model in semi-formal language (BPMN) to represent future business processes using ontology approach. This research continues from previous study which transform requirements list into requirements ontology to formalize the elements such as problem, actor and process. By using requirements ontology as input, rule-based mapping method is proposed to map ontology instances to BPMN elements.

Item Type:	Article
Uncontrolled Keywords:	Business, semi-formal modeling, auto-generate BPMN, ontology-based requirements
Subjects:	H Social Sciences > HF Commerce > HF5001-6182 Business
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	08 Feb 2022 02:38
Last Modified:	08 Feb 2022 02:38
URII:	http://shdl.mmu.edu.my/id/eprint/9065

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