Passage challenges from data-intensive system to knowledge-intensive system related to process mining field-Reference-Cited by-同舟云学术

Passage challenges from data-intensive system to knowledge-intensive system related to process mining field

Published:2019 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the ArabWIC 6th Annual International Conference Research Track on - ArabWIC 2019
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Author:

Lamghari Zineb¹,Radgui Maryam²,Saidi Rajaa²,Rahmani Moulay Driss¹

Affiliation:

1. LRIT associated unit to CNRST (URAC 29), Rabat IT Center, Mohammed V University in Rabat, Morocco

2. LRIT associated unit to CNRST (URAC 29), Rabat IT Center, Mohammed V University in Rabat, Morocco and SI2M Laboratory, National Institute of Statistics and Applied Economics, Rabat, Morocco

Publisher

ACM Press

Reference49 articles.

1. Wil Van Der Aalst, et al. 2011. Process mining manifesto. In International Conference on Business Process Management, Springer, Berlin, Heidelberg, 99, 169--194.DOI: https://doi.org/10.1007/978-3-642-28108-2_19

2. Hind R'bigui and Chiwoon Cho. 2017. The state-of-the-art of business process mining challenges. International Journal of Business Process Integration and Management, 8, 285. DOI: 10.1504/IJBPIM.2017.10009731.

3. Svein Hallsteinsen, Mike Hinchey, Sooyong Park and KlausSchmid.2008. Dynamic Software Product Lines. Computer41, 4, 93--95, (April 2008). Doi: 10.1109/MC.2008.123

4. Evan Rosen. 2011. Every Worker Is a Knowledge Worker. (January 2011). Bloomberg Business Accessed 14 Nov 2015 from https://www.bloomberg.com/news/articles/2011-01-11/every-worker-is-aknowledge- worker

5. Wil Van der Aalst. 2013. Process mining. In Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer, Eindhoven.

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