A context-aware recommendation system for improving manufacturing process modeling-Reference-Cited by-同舟云学术

A context-aware recommendation system for improving manufacturing process modeling

Published:2021-10-18 Issue: Volume: Page:
ISSN:0956-5515
Container-title:Journal of Intelligent Manufacturing
language:en
Short-container-title:J Intell Manuf

Author:

Wang Jiaxing,Gao Sibin,Tang Zhejun,Tan Dapeng^ORCID,Cao Bin,Fan Jing

Funder

National Key Research & Development Program of China

National Natural Science Foundation of China

Natural Science Foundation of Zhejiang Province

China Postdoctoral Science Foundation

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Industrial and Manufacturing Engineering,Software

Link

https://link.springer.com/content/pdf/10.1007/s10845-021-01854-4.pdf

Reference69 articles.

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2. Ahn, H., & Chang, T. W. (2019). A similarity-based hierarchical clustering method for manufacturing process models. Sustainability, 11(9), 2560. https://doi.org/10.3390/SU11092560

3. Augsten, N., Böhlen, M., & Gamper, J. (2008). The pq-gram distance between ordered labeled trees. ACM Transactions on Database Systems (TODS), 35(1), 1–36. https://doi.org/10.1145/1670243.1670247

4. Awadid, A., Nurcan, S., & Ghannouchi, S. A. (2017). Towards a decision-support system for selecting the appropriate business process modeling formalism: A context-aware roadmap. In Enterprise, Business-Process and Information Systems Modeling (pp. 239–256): Springer. https://doi.org/10.1007/978-3-319-59466-8_15.

5. Bobek, S., Nalepa, G. J., & Grodzki, O. (2014). Integration of activity modeller with Bayesian network based recommender for business processes. In KESE@ ECAI.

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