Towards Smart Incident Management Under Human Resource Constraints for an IoT-BPM Hybrid Architecture
| Autor: | Khalid Benali, Abir Ismaili-Alaoui, Jamal Baïna, Karim Baïna |
|---|---|
| Přispěvatelé: | Web Scale Trustworthy Collaborative Service Systems (COAST), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Networks, Systems and Services (LORIA - NSS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Ecole Nationale Supérieure d'Informatique et d'Analyses des Systèmes (ENSIAS), Université Mohamed V - Souissi, Angel Assistance, Hai Jin, Qingyang Wang, Liang-Jie Zhang, Université Mohammed V de Rabat [Agdal] (UM5) |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
IoT
Business process Computer science Distributed computing Enterprise architecture 02 engineering and technology computer.software_genre Scheduling (computing) Business process management Machine Learning 020204 information systems 0202 electrical engineering electronic engineering information engineering Business logic [INFO]Computer Science [cs] business.industry Scheduling [INFO.INFO-WB]Computer Science [cs]/Web Physical layer Incident management BPM Genetic algorithm Genetic Al- gorithm Unsupervised learning 020201 artificial intelligence & image processing Web service business computer |
| Zdroj: | International Conference on Web Services ICWS 2018-25th International Conference on Web Services ICWS 2018-25th International Conference on Web Services, Jun 2018, Seattle, United States. pp.457-471, ⟨10.1007/978-3-319-94289-6_29⟩ Web Services – ICWS 2018 ISBN: 9783319942889 ICWS |
| DOI: | 10.1007/978-3-319-94289-6_29⟩ |
| Popis: | International audience; The Internet of Things (IoT) is exploding, and this new technology affects all the layers in any enterprise architecture, from infrastructure to business. To survive this new evolution and make the most out of this paradigm shift, a communication channel must be created between Business Process Management (BPM) domain and IoT domain in order to bridge the gap between the business layer and the IoT physical layer. The allocation of business process resources to IoT events is an important step towards an end-to-end IoT-BPM integration approach to assist organizations in their scheduling and incident management journey. In this paper, we propose a combination approach which is based on (i) unsupervised machine learning algorithms to generate clusters of priorities, used to estimate incoming events priority, and to ensure a learning feedback loop that feeds forward insight to continuously adjust decisions made at each layer, and (ii) genetic algorithm (GA) to guarantee the assignment of the most critical IoT generated event to the qualified human resource while respecting several constraints such as resource availability and reliability, and taking into consideration the priority of each event that launch process instances. A case study is presented and the obtained results from our experimentations demonstrate the benefit of our approach and allowed us to confirm the efficiency of our assumptions. |
| Databáze: | OpenAIRE |
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