1. Exploiting CBOW and LSTM Models to Generate Trace Representation for Process Mining
- Author
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Hong-Nhung Bui, Tri-Thanh Nguyen, Quang-Thuy Ha, Hien-Hanh Nguyen, and Trong-Sinh Vu
- Subjects
050101 languages & linguistics ,Exploit ,Computer science ,business.industry ,Deep learning ,05 social sciences ,Representation (systemics) ,Process mining ,02 engineering and technology ,Machine learning ,computer.software_genre ,Field (computer science) ,Dimension (vector space) ,Bag-of-words model ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,0501 psychology and cognitive sciences ,Artificial intelligence ,business ,computer ,TRACE (psycholinguistics) - Abstract
In the field of process mining, one of the challenges of the trace representation problem is to exploit a lot of potentially useful information within the traces while keeping a low dimension of the corresponding vector space. Motivated by the initial results of applying the deep neural networks for producing trace representation, in this paper, we continue to study and apply two more advanced models of deep learning, i.e., Continuous Bag of Words and Long short-term memory, for generating the trace representation. The experimental results have achieved significant improvement, i.e., not only showing the close relationship between the activities in a trace but also helping to reduce the dimension of trace representation.
- Published
- 2020