Adept_flex—Supporting Dynamic Changes of Workflows Without Losing Control
Journal of Intelligent Information Systems - Special issue on workflow management systems
Inheritance of workflows: an approach to tackling problems related to change
Theoretical Computer Science
Artificial Intelligence: Structures and Strategies for Complex Problem Solving (5th Edition)
Artificial Intelligence: Structures and Strategies for Complex Problem Solving (5th Edition)
Workflow Mining: Discovering Process Models from Event Logs
IEEE Transactions on Knowledge and Data Engineering
Adaptive Process Management with ADEPT2
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Process Mining, Discovery, and Integration using Distance Measures
ICWS '06 Proceedings of the IEEE International Conference on Web Services
Rediscovering workflow models from event-based data using little thumb
Integrated Computer-Aided Engineering
Data & Knowledge Engineering
Mining Process Variants: Goals and Issues
SCC '08 Proceedings of the 2008 IEEE International Conference on Services Computing - Volume 2
Discovering Reference Process Models by Mining Process Variants
ICWS '08 Proceedings of the 2008 IEEE International Conference on Web Services
On Measuring Process Model Similarity Based on High-Level Change Operations
ER '08 Proceedings of the 27th International Conference on Conceptual Modeling
Handbook of Parametric and Nonparametric Statistical Procedures
Handbook of Parametric and Nonparametric Statistical Procedures
What are the Problem Makers: Ranking Activities According to their Relevance for Process Changes
ICWS '09 Proceedings of the 2009 IEEE International Conference on Web Services
OTM'10 Proceedings of the 2010 international conference on On the move to meaningful internet systems - Volume Part I
Merging business process models
OTM'10 Proceedings of the 2010 international conference on On the move to meaningful internet systems - Volume Part I
Survey paper: Refactoring large process model repositories
Computers in Industry
Editorial: Mining business process variants: Challenges, scenarios, algorithms
Data & Knowledge Engineering
What BPM technology can do for healthcare process support
AIME'11 Proceedings of the 13th conference on Artificial intelligence in medicine
An automation support for creating configurable process models
WISE'11 Proceedings of the 12th international conference on Web information system engineering
Comparison and retrieval of process models using related cluster pairs
Computers in Industry
Ensuring correctness during process configuration via partner synthesis
Information Systems
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
Repairing process models to reflect reality
BPM'12 Proceedings of the 10th international conference on Business Process Management
On Utilizing Web Service Equivalence for Supporting the Composition Life Cycle
International Journal of Web Services Research
Start time and duration distribution estimation in semi-structured processes
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Configuring business process models
ACM SIGSOFT Software Engineering Notes
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Recently, a new generation of adaptive Process-Aware Information Systems (PAISs) has emerged, which enables structural process changes during runtime. Such flexibility, in turn, leads to a large number of process variants derived from the same model, but differing in structure. Generally, such variants are expensive to configure and maintain. This paper provides a heuristic search algorithm which fosters learning from past process changes by mining process variants. The algorithm discovers a reference model based on which the need for future process configuration and adaptation can be reduced. It additionally provides the flexibility to control the process evolution procedure, i.e., we can control to what degree the discovered reference model differs from the original one. As benefit, we cannot only control the effort for updating the reference model, but also gain the flexibility to perform only the most important adaptations of the current reference model. Our mining algorithm is implemented and evaluated by a simulation using more than 7000 process models. Simulation results indicate strong performance and scalability of our algorithm even when facing large-sized process models.