Utility-Based Control Flow Discovery from Business Process Event Logs

2015 ◽  
pp. 69-83
Author(s):  
Kritika Anand ◽  
Nisha Gupta ◽  
Ashish Sureka
Keyword(s):  
Business Process ◽  
Control Flow ◽  
Event Logs ◽  
Process Event

scholarly journals Automatic Repair of Same-Timestamp Errors in Business Process Event Logs

2020 ◽  
pp. 327-345 ◽  
Author(s):  
Raffaele Conforti ◽  
Marcello La Rosa ◽  
Arthur H. M. ter Hofstede ◽  
Adriano Augusto
Keyword(s):  
Business Process ◽  
Event Logs ◽  
Process Event

Business Process Event Logs and Visualization

2019 ◽  
pp. 398-409
Author(s):  
Marlon Dumas ◽  
Jan Mendling
Keyword(s):  
Business Process ◽  
Event Logs ◽  
Process Event

Detecting anomalies in business process event logs using statistical leverage

2021 ◽  
Vol 549 ◽  
pp. 53-67
Author(s):  
Jonghyeon Ko ◽  
Marco Comuzzi
Keyword(s):  
Business Process ◽  
Event Logs ◽  
Process Event

scholarly journals Local Concurrency Detection in Business Process Event Logs

2019 ◽  
Vol 19 (1) ◽  
pp. 1-23 ◽  
Author(s):  
Abel Armas-Cervantes ◽  
Marlon Dumas ◽  
Marcello La Rosa ◽  
Abderrahmane Maaradji
Keyword(s):  
Business Process ◽  
Event Logs ◽  
Process Event

scholarly journals Detecting Complex Control-Flow Constructs for Choosing Process Discovery Techniques

2019 ◽  
Vol 9 (1) ◽  
pp. 1389-1393
Keyword(s):  
Business Process ◽  
Free Choice ◽  
Process Mining ◽  
Control Flow ◽  
Process Models ◽  
Event Data ◽  
Process Discovery ◽  
Event Logs ◽  
Complex Process ◽  
Mining Algorithms

Process models are the analytical illustration of an organization’s activity. They are very primordial to map out the current business process of an organization, build a baseline of process enhancement and construct future processes where the enhancements are incorporated. To achieve this, in the field of process mining, algorithms have been proposed to build process models using the information recorded in the event logs. However, for complex process configurations, these algorithms cannot correctly build complex process structures. These structures are invisible tasks, non-free choice constructs, and short loops. The ability of each discovery algorithm in discovering the process constructs is different. In this work, we propose a framework responsible of detecting from event logs the complex constructs existing in the data. By identifying the existing constructs, one can choose the process discovery techniques suitable for the event data in question. The proposed framework has been implemented in ProM as a plugin. The evaluation results demonstrate that the constructs can correctly be identified.

Filtering Out Infrequent Behavior from Business Process Event Logs

2017 ◽  
Vol 29 (2) ◽  
pp. 300-314 ◽  
Author(s):  
Raffaele Conforti ◽  
Marcello La Rosa ◽  
Arthur H.M. ter Hofstede
Keyword(s):  
Business Process ◽  
Event Logs ◽  
Process Event
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