A Compact Trace Representation Using Deep Neural Networks for Process Mining
| Autor: | Tri-Thanh Nguyen, Quang-Thuy Ha, Hong-Nhung Bui, Thi-Cham Nguyen, Trong-Sinh Vu |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Process modeling
Artificial neural network Computer science Representation (systemics) Process mining 02 engineering and technology computer.software_genre Business process discovery Dimension (vector space) 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining Cluster analysis computer TRACE (psycholinguistics) |
| Zdroj: | KSE |
| DOI: | 10.1109/kse.2019.8919355 |
| Popis: | In process mining, trace representation has a significant effect on the process discovery problem. The challenge is to get highly informative but low-dimensional vector space from event logs. This is required to improve the quality of the trace clustering problem for generating the process models clear enough to inspect. Though traditional trace representation methods have specific advantages, their vector space often has a big number of dimensions. In this paper, we address this problem by proposing a new trace representation method based on the deep neural networks. Experimental results prove our proposal not only is better than the alternatives, but also significantly helps to reduce the dimension of trace representation. |
| Databáze: | OpenAIRE |
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