relation: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/4042/
title: Exploiting CBOW and LSTM Models to Generate Trace Representation for Process Mining
creator: Bui, Hong-Nhung
creator: Vu, Trong-Sinh
creator: Nguyen, Hien-Hanh
creator: Nguyen, Tri-Thanh
creator: Ha, Quang-Thuy
subject: Information Technology (IT)
description: 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.
date: 2020-03-03
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Bui, Hong-Nhung and Vu, Trong-Sinh and Nguyen, Hien-Hanh and Nguyen, Tri-Thanh and Ha, Quang-Thuy  (2020) Exploiting CBOW and LSTM Models to Generate Trace Representation for Process Mining.  In: Asian Conference on Intelligent Information and Database Systems, 23-26 March, 2020, Phuket, Thailand.     
relation: http://dx.doi.org/10.1007/978-981-15-3380-8_4
relation: 10.1007/978-981-15-3380-8_4