Free choice Petri nets
Discovering models of software processes from event-based data
ACM Transactions on Software Engineering and Methodology (TOSEM)
Deriving Petri Nets from Finite Transition Systems
IEEE Transactions on Computers
Inductive Inference: Theory and Methods
ACM Computing Surveys (CSUR)
Mining Process Models from Workflow Logs
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Inductive Inference, DFAs, and Computational Complexity
AII '89 Proceedings of the International Workshop on Analogical and Inductive Inference
Automating the Discovery of As-Is Business Process Models: Probabilistic and Algorithmic Approaches
Information Systems Research
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
Genetic process mining: an experimental evaluation
Data Mining and Knowledge Discovery
Business process mining: An industrial application
Information Systems
Rediscovering workflow models from event-based data using little thumb
Integrated Computer-Aided Engineering
Mining process models with non-free-choice constructs
Data Mining and Knowledge Discovery
Conformance checking of processes based on monitoring real behavior
Information Systems
Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
ProM 4.0: comprehensive support for real process analysis
ICATPN'07 Proceedings of the 28th international conference on Applications and theory of Petri nets and other models of concurrency
Fuzzy mining: adaptive process simplification based on multi-perspective metrics
BPM'07 Proceedings of the 5th international conference on Business process management
Process mining based on regions of languages
BPM'07 Proceedings of the 5th international conference on Business process management
BPM'06 Proceedings of the 4th international conference on Business Process Management
Discovery and analysis of e-mail-driven business processes
Information Systems
Discovering process models from event multiset
Expert Systems with Applications: An International Journal
Implementing design principles for collaborative ERP systems
DESRIST'12 Proceedings of the 7th international conference on Design Science Research in Information Systems: advances in theory and practice
Multidimensional process mining: a flexible analysis approach for health services research
Proceedings of the Joint EDBT/ICDT 2013 Workshops
Modeling Data for Enterprise Systems with Memories
Journal of Database Management
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Processes are everywhere. Organizations have business processes to manufacture products, provide services, purchase goods, handle applications, etc. Also in our daily lives we are involved in a variety of processes, for example when we use our car or when we book a trip via the Internet. Although such operational processes are omnipresent, they are at the same time intangible. Unlike a product or a piece of data, processes are less concrete because of their dynamic nature. However, more and more information about these processes is captured in the form of event logs. Contemporary systems ranging from copiers and medical devices to enterprise information systems and cloud infrastructures record events. These events can be used to make processes visible. Using process mining techniques it is possible to discover processes. This provides the insights necessary to manage, control, and improve processes. Process mining has been successfully applied in a variety of domains ranging from healthcare and e-business to high-tech systems and auditing. Despite these successes, there are still many challenges as process discovery shows that the real processes are more "spaghetti-like" than people like to think. It is still very difficult to capture the complex reality in a suitable model. Given the nature of these challenges, techniques originating from Computational Intelligence may assist in the discovery of complex processes.