.png){kind=icon}
返回
Process mining and path similarity analysis
.png){kind=icon}
OA
AI
DOI：10.1016/j.iswa.2026.200660.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This article proposes a method to leverage process mining techniques to analyse real-life event logs and give insights to managers to improve process performance. For this purpose, we adopt the Levenshtein distance and k-medoids clustering, to identify representative prototype traces for process variants. Then, we introduce a novel Composite Similarity Score, integrating graph-based and attribute-based measures, to assess trace conformance to prototypes. Thus, anomalous traces can be identified by means of outlier detection, revealing significant deviations in duration and process complexity. Key findings highlight prolonged durations and bottlenecks, suggesting targeted process optimization opportunities. By deriving a standardized to-be process model from prototypes, we face process standardization, to allow institutions and enterprises to enhance efficiency, reduce cancellations, and improve decision-making. We test this method on loan application BPI Challenge 2017 dataset.
Keyword:
Path similarity
K-medoids clustering
Mahalanobis distance
Outlier detection
Process standardization
Loan application process
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
I
IF:
4.3
论文数:
92
被引数:
0
机构
引用论文
.png){kind=icon}
分享收藏分享收藏Intervention strategies for improving business process outcomes: A causal rule mining approach改进业务流程结果干预策略：一种因果规则挖掘方法
PREAI
分享收藏Security event-triggered control for uncertain NCSs against multi-channel optimal jamming attacks不确定ncs对抗多通道最优干扰攻击的安全事件触发控制
PREAI
分享收藏
查看更多内容