Open AccessA Language and Methodology based on Scenarios, Grammars and Views, for Administrative Business Processes Modelling
Author(s) -
Milliam Maxime Zekeng Ndadji,
Maurice Tchoupé Tchendji,
Clémentin Tayou Djamegni,
Didier Parigot
Publication year - 2020
Publication title -
paradigmplus
Language(s) - English
Resource type - Journals
ISSN - 2711-4627
DOI - 10.55969/paradigmplus.v1n3a1
Subject(s) - computer science , business process modeling , business process , artifact centric business process model , modularity (biology) , process modeling , rule based machine translation , business process model and notation , business process management , business rule , modeling language , software engineering , formalism (music) , business domain , programming language , data science , artificial intelligence , work in process , engineering , art , musical , operations management , software , biology , visual arts , genetics
In Business Process Management (BPM), process modelling has been solved in various ways. However, there are no commonly accepted modelling tools (languages). Some of them are criticized for their inability to capture both the lifecycle, informational and organizational models of processes. For some others, process modelling is generally done using a single graph; this does not facilitate modularity, maintenance and scalability. In addition, some of these languages are very general; hence, their application to specific domain processes (such as administrative processes) is very complex. In this paper, we present a new language and a new methodology, dedicated to administrative process modelling. This language is based on a variant of attributed grammars and is able to capture the lifecycle, informational and organizational models of such processes. Also, it proposes a simple graphical formalism allowing to model each process's execution scenario as an annotated tree (modularity). In the new language, a particular emphasis is put on modelling (using "views") the perceptions that actors have on processes and their data.