scholarly journals Process Discovery Algorithms Using Numerical Abstract Domains

2014 ◽  
Vol 26 (12) ◽  
pp. 3064-3076 ◽  
Author(s):  
Josep Carmona ◽  
Jordi Cortadella
Keyword(s):  
Process Discovery ◽  
Numerical Abstract Domains ◽  
Abstract Domains ◽  
Discovery Algorithms

scholarly journals Improving the performance of process discovery algorithms by instance selection

2020 ◽  
Vol 17 (3) ◽  
pp. 927-958
Author(s):  
Mohammadreza Sani ◽  
Sebastiaan van Zelst ◽  
Aalst van der
Keyword(s):  
Process Model ◽  
Business Processes ◽  
Process Models ◽  
Instance Selection ◽  
Event Data ◽  
Process Discovery ◽  
Selection Strategies ◽  
Speed Up ◽  
The Right ◽  
Discovery Algorithms

Process discovery algorithms automatically discover process models based on event data that is captured during the execution of business processes. These algorithms tend to use all of the event data to discover a process model. When dealing with large event logs, it is no longer feasible using standard hardware in limited time. A straightforward approach to overcome this problem is to down-size the event data by means of sampling. However, little research has been conducted on selecting the right sample, given the available time and characteristics of event data. This paper evaluates various subset selection methods and evaluates their performance on real event data. The proposed methods have been implemented in both the ProM and the RapidProM platforms. Our experiments show that it is possible to considerably speed up discovery using instance selection strategies. Furthermore, results show that applying biased selection of the process instances compared to random sampling will result in simpler process models with higher quality.

scholarly journals A practical construction for decomposing numerical abstract domains

2018 ◽  
Vol 2 (POPL) ◽  
pp. 1-28 ◽  
Author(s):  
Gagandeep Singh ◽  
Markus Püschel ◽  
Martin Vechev
Keyword(s):  
Numerical Abstract Domains ◽  
Abstract Domains

Quality Dimensions in Process Discovery: The Importance of Fitness, Precision, Generalization and Simplicity

2014 ◽  
Vol 23 (01) ◽  
pp. 1440001 ◽  
Author(s):  
J. C. A. M. Buijs ◽  
B. F. van Dongen ◽  
W. M. P. van der Aalst
Keyword(s):  
Process Models ◽  
Discovery Process ◽  
Process Discovery ◽  
Event Logs ◽  
Quality Dimensions ◽  
Discovery Algorithms

Process discovery algorithms typically aim at discovering process models from event logs that best describe the recorded behavior. Often, the quality of a process discovery algorithm is measured by quantifying to what extent the resulting model can reproduce the behavior in the log, i.e. replay fitness. At the same time, there are other measures that compare a model with recorded behavior in terms of the precision of the model and the extent to which the model generalizes the behavior in the log. Furthermore, many measures exist to express the complexity of a model irrespective of the log.In this paper, we first discuss several quality dimensions related to process discovery. We further show that existing process discovery algorithms typically consider at most two out of the four main quality dimensions: replay fitness, precision, generalization and simplicity. Moreover, existing approaches cannot steer the discovery process based on user-defined weights for the four quality dimensions.This paper presents the ETM algorithm which allows the user to seamlessly steer the discovery process based on preferences with respect to the four quality dimensions. We show that all dimensions are important for process discovery. However, it only makes sense to consider precision, generalization and simplicity if the replay fitness is acceptable.

An Integrated Approach for Discovering Process Models According to Business Process Types

2021 ◽  
Vol 16 ◽  
pp. 1-14
Author(s):  
Zineb Lamghari
Keyword(s):  
Business Process ◽  
Domain Knowledge ◽  
Process Model ◽  
Process Mining ◽  
Integrated Approach ◽  
Process Models ◽  
Management Support ◽  
Event Data ◽  
Process Discovery ◽  
Discovery Algorithms

Process discovery technique aims at automatically generating a process model that accurately describes a Business Process (BP) based on event data. Related discovery algorithms consider recorded events are only resulting from an operational BP type. While the management community defines three BP types, which are: Management, Support and Operational. They distinguish each BP type by different proprieties like the main business process objective as domain knowledge. This puts forward the lack of process discovery technique in obtaining process models according to business process types (Management and Support). In this paper, we demonstrate that business process types can guide the process discovery technique in generating process models. A special interest is given to the use of process mining to deal with this challenge.

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