LogRank: An Approach to Sample Business Process Event Log for Efficient Discovery

2018 ◽  
pp. 415-425 ◽  
Author(s):  
Cong Liu ◽  
Yulong Pei ◽  
Qingtian Zeng ◽  
Hua Duan
Keyword(s):  
Business Process ◽  
Event Log ◽  
Process Event

Decision Support Using Belief Network Constructed from Business Process Event Log

Informatica ◽  
2017 ◽  
Vol 28 (4) ◽  
pp. 687-701
Author(s):  
Titas Savickas ◽  
Olegas Vasilecas
Keyword(s):  
Decision Support ◽  
Business Process ◽  
Belief Network ◽  
Event Log ◽  
Process Event

scholarly journals Towards IoT-driven Process Event Log Generation for Conformance Checking in Smart Factories

2020 ◽  
Author(s):  
Ronny Seiger ◽  
Francesca Zerbato ◽  
Andrea Burattin ◽  
Luciano Garcia-Banuelos ◽  
Barbara Weber
Keyword(s):  
Conformance Checking ◽  
Event Log ◽  
Process Event ◽  
Smart Factories

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

scholarly journals Temporal Logics Over Finite Traces with Uncertainty

2020 ◽  
Vol 34 (06) ◽  
pp. 10218-10225 ◽  
Author(s):  
Fabrizio M Maggi ◽  
Marco Montali ◽  
Rafael Peñaloza
Keyword(s):  
Decision Making ◽  
Temporal Logic ◽  
Business Process ◽  
Dynamic Systems ◽  
Real Life ◽  
Temporal Logics ◽  
Business Process Modelling ◽  
Process Discovery ◽  
Event Log ◽  
Computational Properties

Temporal logics over finite traces have recently seen wide application in a number of areas, from business process modelling, monitoring, and mining to planning and decision making. However, real-life dynamic systems contain a degree of uncertainty which cannot be handled with classical logics. We thus propose a new probabilistic temporal logic over finite traces using superposition semantics, where all possible evolutions are possible, until observed. We study the properties of the logic and provide automata-based mechanisms for deriving probabilistic inferences from its formulas. We then study a fragment of the logic with better computational properties. Notably, formulas in this fragment can be discovered from event log data using off-the-shelf existing declarative process discovery techniques.

Business Process Event Logs and Visualization

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

Anomaly Detection in Business Process Event Using KNN Algorithm

2021 ◽  
pp. 189-199
Author(s):  
B. Sowmia ◽  
G. Suseendran
Keyword(s):  
Anomaly Detection ◽  
Business Process ◽  
Process Event
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