Discovering models of software processes from event-based data
ACM Transactions on Software Engineering and Methodology (TOSEM)
A Machine Learning Approach to Workflow Management
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Mining Process Models from Workflow Logs
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Workflow mining: a survey of issues and approaches
Data & Knowledge Engineering
Workflow Mining: Discovering Process Models from Event Logs
IEEE Transactions on Knowledge and Data Engineering
Rediscovering workflow models from event-based data using little thumb
Integrated Computer-Aided Engineering
Evolving Petri nets with a genetic algorithm
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Soundness and separability of workflow nets in the stepwise refinement approach
ICATPN'03 Proceedings of the 24th international conference on Applications and theory of Petri nets
Genetic process mining: an experimental evaluation
Data Mining and Knowledge Discovery
Mining process models with non-free-choice constructs
Data Mining and Knowledge Discovery
Conformance checking of processes based on monitoring real behavior
Information Systems
Quantifying process equivalence based on observed behavior
Data & Knowledge Engineering
Discovery, Verification and Conformance of Workflows with Cancellation
ICGT '08 Proceedings of the 4th international conference on Graph Transformations
Generating Business Process Models from Object Behavior Models
Information Systems Management
Discovering expressive process models from noised log data
IDEAS '09 Proceedings of the 2009 International Database Engineering & Applications Symposium
Process mining framework for software processes
ICSP'07 Proceedings of the 2007 international conference on Software process
σ-algorithm: structured workflow process mining through amalgamating temporal workcases
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Mining invisible tasks from event logs
APWeb/WAIM'07 Proceedings of the joint 9th Asia-Pacific web and 8th international conference on web-age information management conference on Advances in data and web management
Approaching process mining with sequence clustering: experiments and findings
BPM'07 Proceedings of the 5th international conference on Business process management
Transforming object-oriented models to process-oriented models
BPM'07 Proceedings of the 2007 international conference on Business process management
Agent assignment for process management: agent performance evaluation
Proceedings of the 7th International Conference on Frontiers of Information Technology
A fresh look at precision in process conformance
BPM'10 Proceedings of the 8th international conference on Business process management
Process mining and verification of properties: an approach based on temporal logic
OTM'05 Proceedings of the 2005 Confederated international conference on On the Move to Meaningful Internet Systems - Volume >Part I
BPM'06 Proceedings of the 4th international conference on Business Process Management
Process equivalence: comparing two process models based on observed behavior
BPM'06 Proceedings of the 4th international conference on Business Process Management
Towards synthesis of petri nets from scenarios
ICATPN'06 Proceedings of the 27th international conference on Applications and Theory of Petri Nets and Other Models of Concurrency
Projection approaches to process mining using region-based techniques
Data Mining and Knowledge Discovery
Conformance testing: measuring the fit and appropriateness of event logs and process models
BPM'05 Proceedings of the Third international conference on Business Process Management
Mining staff assignment rules from event-based data
BPM'05 Proceedings of the Third international conference on Business Process Management
Transactions on Petri Nets and Other Models of Concurrency V
Discovering process models from event multiset
Expert Systems with Applications: An International Journal
Applying process analysis to the italian egovernment enterprise architecture
WS-FM'11 Proceedings of the 8th international conference on Web Services and Formal Methods
Light Region-based Techniques for Process Discovery
Fundamenta Informaticae - Applications and Theory of Petri Nets and Other Models of Concurrency, 2010
An SMT-Based discovery algorithm for c-nets
PETRI NETS'12 Proceedings of the 33rd international conference on Application and Theory of Petri Nets
On profiles and footprints --- relational semantics for petri nets
PETRI NETS'12 Proceedings of the 33rd international conference on Application and Theory of Petri Nets
Online techniques for dealing with concept drift in process mining
IDA'12 Proceedings of the 11th international conference on Advances in Intelligent Data Analysis
Simplifying discovered process models in a controlled manner
Information Systems
A comparative study of dimensionality reduction techniques to enhance trace clustering performances
Expert Systems with Applications: An International Journal
Amending C-net discovery algorithms
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Discovering block-structured process models from event logs - a constructive approach
PETRI NETS'13 Proceedings of the 34th international conference on Application and Theory of Petri Nets and Concurrency
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The topic of process mining has attracted the attention of both researchers and tool vendors in the Business Process Management (BPM) space. The goal of process mining is to discover process models from event logs, i.e., events logged by some information system are used to extract information about activities and their causal relations. Several algorithms have been proposed for process mining. Many of these algorithms cannot deal with concurrency. Other typical problems are the presence of duplicate activities, hidden activities, non-free-choice constructs, etc. In addition, real-life logs contain noise (e.g., exceptions or incorrectly logged events) and are typically incomplete (i.e., the event logs contain only a fragment of all possible behaviors). To tackle these problems we propose a completely new approach based on genetic algorithms. As can be expected, a genetic approach is able to deal with noise and incompleteness. However, it is not easy to represent processes properly in a genetic setting. In this paper, we show a genetic process mining approach using the so-called causal matrix as a representation for individuals. We elaborate on the relation between Petri nets and this representation and show that genetic algorithms can be used to discover Petri net models from event logs.