eprintid: 3711
rev_number: 9
eprint_status: archive
userid: 286
dir: disk0/00/00/37/11
datestamp: 2019-12-05 14:00:42
lastmod: 2019-12-05 14:00:42
status_changed: 2019-12-05 14:00:42
type: conference_item
metadata_visibility: show
creators_name: Bui, Hong Nhung
creators_name: Vu, Trong-Sinh
creators_name: Nguyen, Tri Thanh
creators_name: Nguyen, Thi Cham
creators_name: Ha, Quang Thuy
creators_id: nhungbth@hvnh.edu.vn
creators_id: sinhvtr@jaist.ac.jp
creators_id: ntthanh@vnu.edu.vn
creators_id: nthicham@hpmu.edu.vn
creators_id: thuyhq@vnu.edu.vn
title: A Compact Trace Representation Using Deep Neural Networks for Process Mining
ispublished: pub
subjects: IT
divisions: fac_fit
abstract: — In process mining, trace representation has a significant effect on the process discovery problem, the challenge is to get highly informative but low-dimensional of the vector space from event logs. This is required to improve the quality of the trace clustering problem and generate the process models that are easy to understand. Though traditional trace representation methods have specific good effects, their vector space often has a lot of dimensions. In this paper, we address this problem by proposing a new trace representation method based on the deep neural networks. Experimental results show that our method not only outperforms alternatives but also helps significantly reduce the dimension of feature representation.
date: 2019-10-24
date_type: published
full_text_status: none
pres_type: paper
event_title: The 11th IEEE International Conference on Knowledge and Systems Engineering
event_location: Da Nang, Vietnam
event_dates: 24-26 October, 2019
event_type: conference
refereed: FALSE
citation:   Bui, Hong Nhung and Vu, Trong-Sinh and Nguyen, Tri Thanh and Nguyen, Thi Cham and Ha, Quang Thuy  (2019) A Compact Trace Representation Using Deep Neural Networks for Process Mining.  In: The 11th IEEE International Conference on Knowledge and Systems Engineering, 24-26 October, 2019, Da Nang, Vietnam.