An assessment taxonomy for self-adaptation business process solutions

Self-adaptation of business processes has become the focus of several research studies aiming at avoiding a manual adaptation of processes at run-time, which is error-prone and time-consuming. In fact, several contributions addressing the self-adaptation of processes have been proposed in the literature, but none of them has comprehensively studied and analyzed the literature to assess the current state of progress in the self-adaptation of processes. To address this gap, we propose in this paper a comprehensive taxonomy that identifies a set of characteristics to serve as support for the comparative analysis of solutions addressing self-adaptation of processes. Our taxonomy includes 25 characteristics that address relevant questions regarding self-adaptation of processes. While creating our taxonomy, we built on existing literature and involved academic experts from different universities. These experts did not only validate our taxonomy regarding completeness and understandability, but also rectified and enriched it with their knowledge. Finally, we report the application of this taxonomy to evaluate some existing contributions on self-adaptation of processes.

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Publication Details

Journal
Data & Knowledge Engineering
Published
2024-11-07
DOI
https://doi.org/10.1016/j.datak.2024.102374
Citations
2
Primary Topic
Business Process Modeling and Analysis
Type
article
Field-Weighted Citation Impact
1.06
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article

An assessment taxonomy for self-adaptation business process solutions

Imen Ben Said, Éric Andonoff, Mohamed Chaabane, Jamila Oukharijane et al.
2 citations
Data & Knowledge Engineering
Business Process Modeling and Analysis
1.06
article

An assessment taxonomy for self-adaptation business process solutions

Imen Ben Said, Éric Andonoff, Mohamed Chaabane, Jamila Oukharijane, Rafik Bouaziz
article en
2 citations

Abstract

Self-adaptation of business processes has become the focus of several research studies aiming at avoiding a manual adaptation of processes at run-time, which is error-prone and time-consuming. In fact, several contributions addressing the self-adaptation of processes have been proposed in the literature, but none of them has comprehensively studied and analyzed the literature to assess the current state of progress in the self-adaptation of processes. To address this gap, we propose in this paper a comprehensive taxonomy that identifies a set of characteristics to serve as support for the comparative analysis of solutions addressing self-adaptation of processes. Our taxonomy includes 25 characteristics that address relevant questions regarding self-adaptation of processes. While creating our taxonomy, we built on existing literature and involved academic experts from different universities. These experts did not only validate our taxonomy regarding completeness and understandability, but also rectified and enriched it with their knowledge. Finally, we report the application of this taxonomy to evaluate some existing contributions on self-adaptation of processes.

Data & Knowledge EngineeringVol. 155
Université Toulouse III - Paul Sabatier (FR), University of Sfax (TN), Free University of Bozen-Bolzano (IT), Université Toulouse-I-Capitole (FR), Institut de Recherche en Informatique de Toulouse (FR), Digital Research Centre of Sfax (TN), Université Toulouse - Jean Jaurès (FR), Institut Polytechnique de Bordeaux (FR)
Openalex Percentile: Top 16%
Business Process Modeling and Analysis
1.06
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An assessment taxonomy for self-adaptation business process solutions — Imen Ben Said, Éric Andonoff, et al. · Data & Knowledge Engineering (2024) | TGRS Research Map | TGRS