4.6 Article

A visual approach for identification and annotation of business process elements in process descriptions

期刊

COMPUTER STANDARDS & INTERFACES
卷 81, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.csi.2021.103601

关键词

Business process management; Natural language processing; Visually interactive process description

资金

  1. Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior-Brasil (CAPES) [001]

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This paper proposes a user-interactive visual approach to support process comprehension by identifying and annotating core BPMN 2.0 elements in process descriptions. The approach showed promising results in a survey experiment, with 88% of users indicating positive results regarding its usefulness in assisting the process modeling phase. Additionally, a process modeling case study demonstrated a 77% precision in comparison to the original process model.
Business process management (BPM) has been proven to provide several benefits for organizations (e.g., efficiency, agility, governance). However, the effort required for adopting a process-centered approach can be a challenge in different aspects, including financial concerns, organizational changes and time consumption. To achieve this goal, many companies use different approaches, such as document analysis, to be able to discover and understand their business processes. In light of this, in this paper, we propose a user-interactive visual approach to support the process comprehension by identifying and annotating core BPMN 2.0 elements in process descriptions. Specifically, our approach is able to detect sequences of words that indicate the presence of a process element, create a consistent data structure, and expose it as a consumable web service. To evaluate our approach, we conducted a survey experiment, showing promising results in every category evaluated, for which 88% of the users indicated positive results concerning the usefulness of the approach to assist the process modeling phase. Additionally, a process modeling case study shows a designed process model with a precision of 77% of process elements in comparison to its original process model.

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