Ensuring the canonicity of process models
| Autor: | Fabian Pittke, Jan Mendling, Henrik Leopold |
|---|---|
| Přispěvatelé: | Software and Sustainability (S2), Network Institute, Business Informatica |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Information Systems and Management
Process modeling Business process Modeling language Process (engineering) Computer science media_common.quotation_subject Pattern refactoring Canonical representation 02 engineering and technology computer.software_genre Control flow 020204 information systems 0202 electrical engineering electronic engineering information engineering Non-canonicity patterns media_common business.industry 020207 software engineering Ambiguity Conceptual modeling Code refactoring Data mining Software engineering business SDG 12 - Responsible Consumption and Production computer Natural language |
| Zdroj: | Leopold, H, Pittke, F & Mendling, J 2017, ' Ensuring the canonicity of process models ', Data and Knowledge Engineering, vol. 111, pp. 22-38 . https://doi.org/10.1016/j.datak.2017.03.010 Data and Knowledge Engineering, 111, 22-38. Elsevier |
| ISSN: | 0169-023X |
| DOI: | 10.1016/j.datak.2017.03.010 |
| Popis: | Process models play an important role for specifying requirements of business-related software. However, the usefulness of process models is highly dependent on their quality. Recognizing this, researches have proposed various techniques for the automated quality assurance of process models. A considerable shortcoming of these techniques is the assumption that each activity label consistently refers to a single stream of action. If, however, activities textually describe control flow related aspects such as decisions or conditions, the analysis results of these tools are distorted. Due to the ambiguity that is associated with this misuse of natural language, also humans struggle with drawing valid conclusions from such inconsistently specified activities. In this paper, we therefore introduce the notion of canonicity to prevent the mixing of natural language and modeling language. We identify and formalize non-canonical patterns, which we then use to define automated techniques for detecting and refactoring activities that do not comply with it. We evaluated these techniques by the help of four process model collections from industry, which confirmed the applicability and accuracy of these techniques. |
| Databáze: | OpenAIRE |
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