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A method to infer the need to update situations in business process adaptation |
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| Affiliation: | 1. Asia Pacific School of Logistics, Inha University, Republic of Korea;2. Graduate School for International Development and Cooperation, Hiroshima University, Japan;3. Center for Far Eastern Studies, University of Toyama, Japan;4. Graduate School for International Development and Cooperation, Hiroshima University, 1-5-1 Kagamiyama, Higashi-Hiroshima, Hiroshima, 739-8529, Japan;5. Sauder School of Business, University of British Columbia, Canada;3. Heemskerk Innovative Technology BV, Mijnbouwstraat 120, 2628 RX Delft, The Netherlands;4. Delft University of Technology, Department of BioMechanical Engineering, Faculty 3me, Mekelweg 2, 2628 CD Delft, The Netherlands |
| Abstract: | Contextual knowledge is an essential resource for adapting business processes in order to keep them aligned with its goals. A context-based adaptation environment should learn from the dynamism of the context as well as the decisions made, and continuously identify new unforeseen situations. Data mining is a possibility to maintain the analysis of the processes updated. This paper presents a method that infers the need to learn new situations that influence a business process execution. The method is based on the results of the Apriori algorithm application. Case studies were conducted to evaluate the proposal. We observed evidences of context changes over time and the potential to learn with this dynamics through the method proposed. |
| Keywords: | Dynamic adaptation of business processes Context Data mining Apriori |
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