Abstract
The article presents the ontology based knowledge model iKnow that can automatically draw conclusions and integrate aspects of machine learning. Due to the knowledge-intensive nature of the consulting industry, the abstract reasoning based knowledge model can be used specifically for knowledge processing and decision support within a consulting project. There is a multitude of potential applications for iKnow in the realm of consulting. Business process analysis was chosen as a pilot application, since many consulting projects in the problem analysis and problem solving phase, require a comprehensive knowledge of business processes. In this paper it is outlined how iKnow can be used for an automated analysis of business process models. We describe the basic structure of the knowledge model as a business process analyzing tool and present a suitable demonstration. It is worth mentioning that iKnow does not necessarily rely on log-files or other data input from process-supporting IT-systems. In this way, and through the generality of its ontology based structure and reasoning capabilities, it is far more broadly applicable than current process mining solutions.
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Notes
- 1.
The ARIS concept is process-oriented and recommends the perception of operational reality as a goal-oriented cooperation of business processes (Staud 2006).
- 2.
Quality Function Deployment, see Nissen et al. (2017).
- 3.
An ICT class represents a group of certain technologies, see Nissen et al. (2017) in this volume.
- 4.
In this case the QFD (Quality Function Deployment) values 3-5-9 may apply.
- 5.
The AHP (Analytic Hierarchy Process) algorithm was used for weighting the criteria that were prioritized by a user resp. consultant. For a detailed description of the developed method, see Nissen et al. (2017) in this volume.
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Füßl, A., Füßl, F.F., Nissen, V., Streitferdt, D. (2018). A Reasoning Based Knowledge Model for Business Process Analysis. In: Nissen, V. (eds) Digital Transformation of the Consulting Industry. Progress in IS. Springer, Cham. https://doi.org/10.1007/978-3-319-70491-3_13
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