Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Research Article
Authors: Yu, Dongjina; * | Ni, Kea | Li, Zhongyangb | Zhang, Shengyib | Sun, Xiaoxiaoa | Hou, Wenjiea | Ying, Yukea
Affiliations: [a] School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang, China | [b] Zhejiang Cangnan Instrument Group Co., LTD, Cangnan, Zhejiang, China
Correspondence: [*] Corresponding author: Dongjin Yu, School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China. E-mail: yudj@hdu.edu.cn.
Abstract: Process discovery techniques analyze process logs to extract models that characterize the behavior of business processes. In real-life logs, however, noises exist and adversely affect the extraction and thus decrease the understandability of discovered models. In this paper, we propose a novel double granularity filtering method, executed on both the event and trace levels, to detect noises by analyzing the directly-following and parallel relations between events. Based on the probability of an event occurring in a sequence, the infrequent behaviors and redundant events in the logs can be filtered out. In addition, the missing events in parallel blocks are detected to further improve the performance of filtering. Experiments on synthetic logs and five real-life datasets demonstrate that our method significantly outperforms other state-of-the-art methods.
Keywords: Process discovery, process mining, event logs, noise filtering, event dependency, parallel relation
DOI: 10.3233/IDA-230118
Journal: Intelligent Data Analysis, vol. 28, no. 5, pp. 1171-1188, 2024
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
sales@iospress.com
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
info@iospress.nl
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office info@iospress.nl
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
china@iospress.cn
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
如果您在出版方面需要帮助或有任何建, 件至: editorial@iospress.nl