Detection and removal of infrequent behavior from event streams of business processes-Reference-Cited by-同舟云学术

Detection and removal of infrequent behavior from event streams of business processes

Published:2020-05 Issue: Volume:90 Page:101451
ISSN:0306-4379
Container-title:Information Systems
language:en
Short-container-title:Information Systems

Author:

van Zelst Sebastiaan J.,Fani Sani Mohammadreza,Ostovar Alireza,Conforti Raffaele,La Rosa Marcello

Funder

Australian Research Council

Publisher

Elsevier BV

Subject

Hardware and Architecture,Information Systems,Software

Reference26 articles.

1. Process Mining - Data Science in Action;van der Aalst,2016

2. Process Mining: A Database of Applications;Gonella,2017

3. Detecting drift from event streams of unpredictable business processes;Ostovar,2016

4. Characterizing drift from event streams of business processes;Ostovar,2017

5. Detecting sudden and gradual drifts in business processes from execution traces;Maaradji;IEEE Trans. Knowl. Data Eng.,2017

Cited by 18 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Pre-Processing Event Logs by Chaotic Filtering Approaches Based on the Direct Following Relationship;Applied Sciences;2024-08-09

2. Entropy-Based Behavioral Closeness Filtering Chaotic Activity Method;Mathematics;2024-02-24

3. Towards Scalable Process Mining Pipelines;2023 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech);2023-11-14

4. A Systematic Review of Anomaly Detection for Business Process Event Logs;Business & Information Systems Engineering;2023-03-10

5. Online Process Mining: A Systematic Literature Review;Artificial Intelligence and Green Computing;2023