scholarly journals Automated Discovery of Structured Process Models: Discover Structured vs. Discover and Structure

2016 ◽  
pp. 313-329 ◽  
Author(s):  
Adriano Augusto ◽  
Raffaele Conforti ◽  
Marlon Dumas ◽  
Marcello La Rosa ◽  
Giorgio Bruno
Keyword(s):  
Process Models ◽  
Automated Discovery

scholarly journals Automated discovery of structured process models from event logs: The discover-and-structure approach

2018 ◽  
Vol 117 ◽  
pp. 373-392 ◽  
Author(s):  
Adriano Augusto ◽  
Raffaele Conforti ◽  
Marlon Dumas ◽  
Marcello La Rosa ◽  
Giorgio Bruno
Keyword(s):  
Process Models ◽  
Structure Approach ◽  
Event Logs ◽  
Automated Discovery

scholarly journals The RALph miner for automated discovery and verification of resource-aware process models

2020 ◽  
Vol 19 (6) ◽  
pp. 1415-1441
Author(s):  
Cristina Cabanillas ◽  
Lars Ackermann ◽  
Stefan Schönig ◽  
Christian Sturm ◽  
Jan Mendling
Keyword(s):  
Process Model ◽  
Model Verification ◽  
Process Models ◽  
Resource Assignment ◽  
Process Discovery ◽  
Event Logs ◽  
Different Types ◽  
Automated Discovery ◽  
And Performance ◽  
Resource Aware

Abstract Automated process discovery is a technique that extracts models of executed processes from event logs. Logs typically include information about the activities performed, their timestamps and the resources that were involved in their execution. Recent approaches to process discovery put a special emphasis on (human) resources, aiming at constructing resource-aware process models that contain the inferred resource assignment constraints. Such constraints can be complex and process discovery approaches so far have missed the opportunity to represent expressive resource assignments graphically together with process models. A subsequent verification of the extracted resource-aware process models is required in order to check the proper utilisation of resources according to the resource assignments. So far, research on discovering resource-aware process models has assumed that models can be put into operation without modification and checking. Integrating resource mining and resource-aware process model verification faces the challenge that different types of resource assignment languages are used for each task. In this paper, we present an integrated solution that comprises (i) a resource mining technique that builds upon a highly expressive graphical notation for defining resource assignments; and (ii) automated model-checking support to validate the discovered resource-aware process models. All the concepts reported in this paper have been implemented and evaluated in terms of feasibility and performance.

scholarly journals Split miner: automated discovery of accurate and simple business process models from event logs

2018 ◽  
Vol 59 (2) ◽  
pp. 251-284 ◽  
Author(s):  
Adriano Augusto ◽  
Raffaele Conforti ◽  
Marlon Dumas ◽  
Marcello La Rosa ◽  
Artem Polyvyanyy
Keyword(s):  
Business Process ◽  
Process Models ◽  
Business Process Models ◽  
Event Logs ◽  
Automated Discovery

scholarly journals Automated discovery of declarative process models with correlated data conditions

2020 ◽  
Vol 89 ◽  
pp. 101482 ◽  
Author(s):  
Volodymyr Leno ◽  
Marlon Dumas ◽  
Fabrizio Maria Maggi ◽  
Marcello La Rosa ◽  
Artem Polyvyanyy
Keyword(s):  
Correlated Data ◽  
Process Models ◽  
Automated Discovery

scholarly journals Slice, Mine and Dice: Complexity-Aware Automated Discovery of Business Process Models

2013 ◽  
pp. 49-64 ◽  
Author(s):  
Chathura C. Ekanayake ◽  
Marlon Dumas ◽  
Luciano García-Bañuelos ◽  
Marcello La Rosa
Keyword(s):  
Business Process ◽  
Process Models ◽  
Business Process Models ◽  
Automated Discovery

scholarly journals Artifact Lifecycle Discovery

2015 ◽  
Vol 24 (01) ◽  
pp. 1550001 ◽  
Author(s):  
Viara Popova ◽  
Dirk Fahland ◽  
Marlon Dumas
Keyword(s):  
Business Process ◽  
Process Model ◽  
Business Processes ◽  
Process Mining ◽  
Real Life ◽  
Process Models ◽  
Reverse Engineer ◽  
Wide Range ◽  
Open Source Framework ◽  
Automated Discovery

Artifact-centric modeling is an approach for capturing business processes in terms of so-called business artifacts — key entities driving a company's operations and whose lifecycles and interactions define an overall business process. This approach has been shown to be especially suitable in the context of processes where one-to-many or many-to-many relations exist between the entities involved in the process. As a contribution towards building up a body of methods to support artifact-centric modeling, this article presents a method for automated discovery of artifact-centric process models starting from logs consisting of flat collections of event records. We decompose the problem in such a way that a wide range of existing (non-artifact-centric) automated process discovery methods can be reused in a flexible manner. The presented methods are implemented as a package for ProM, a generic open-source framework for process mining. The methods have been applied to reverse-engineer an artifact-centric process model starting from logs of a real-life business process.

Parallel algorithms for the automated discovery of declarative process models

2018 ◽  
Vol 74 ◽  
pp. 136-152 ◽  
Author(s):  
Fabrizio Maria Maggi ◽  
Claudio Di Ciccio ◽  
Chiara Di Francescomarino ◽  
Taavi Kala
Keyword(s):  
Parallel Algorithms ◽  
Process Models ◽  
Automated Discovery

scholarly journals Controlled automated discovery of collections of business process models

2014 ◽  
Vol 46 ◽  
pp. 85-101 ◽  
Author(s):  
Luciano García-Bañuelos ◽  
Marlon Dumas ◽  
Marcello La Rosa ◽  
Jochen De Weerdt ◽  
Chathura C. Ekanayake
Keyword(s):  
Business Process ◽  
Process Models ◽  
Business Process Models ◽  
Automated Discovery

scholarly journals Automated Discovery of Process Models from Event Logs: Review and Benchmark

2019 ◽  
Vol 31 (4) ◽  
pp. 686-705 ◽  
Author(s):  
Adriano Augusto ◽  
Raffaele Conforti ◽  
Marlon Dumas ◽  
Marcello La Rosa ◽  
Fabrizio Maria Maggi ◽  
...  
Keyword(s):  
Process Models ◽  
Event Logs ◽  
Automated Discovery
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