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Exploiting CBOW and LSTM Models to Generate Trace Representation for Process Mining
- Source :
- Communications in Computer and Information Science ISBN: 9789811533792, ACIIDS (Companion)
- Publication Year :
- 2020
- Publisher :
- Springer Singapore, 2020.
-
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.
- 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)
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- Communications in Computer and Information Science ISBN: 9789811533792, ACIIDS (Companion)
- Accession number :
- edsair.doi...........6dcd7cf284a7c50d0e8abce97ef78bad