Class-based n-gram models of natural language
Computational Linguistics
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
Distributed and Parallel Databases
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Text-Retrieval: Theory and Practice
Proceedings of the IFIP 12th World Computer Congress on Algorithms, Software, Architecture - Information Processing '92, Volume 1 - Volume I
On Structured Workflow Modelling
CAiSE '00 Proceedings of the 12th International Conference on Advanced Information Systems Engineering
Relaxed Soundness of Business Processes
CAiSE '01 Proceedings of the 13th International Conference on Advanced Information Systems Engineering
Artificial Intelligence: Structures and Strategies for Complex Problem Solving (5th Edition)
Artificial Intelligence: Structures and Strategies for Complex Problem Solving (5th Edition)
Transforming BPEL into Annotated Deterministic Finite State Automata for Service Discovery
ICWS '04 Proceedings of the IEEE International Conference on Web Services
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
When are two workflows the same?
CATS '05 Proceedings of the 2005 Australasian symposium on Theory of computing - Volume 41
Process Mining, Discovery, and Integration using Distance Measures
ICWS '06 Proceedings of the IEEE International Conference on Web Services
A configurable reference modelling language
Information Systems
IT support for healthcare processes - premises, challenges, perspectives
Data & Knowledge Engineering
Rediscovering workflow models from event-based data using little thumb
Integrated Computer-Aided Engineering
Conformance checking of processes based on monitoring real behavior
Information Systems
Data & Knowledge Engineering
How Much Language Is Enough? Theoretical and Practical Use of the Business Process Modeling Notation
CAiSE '08 Proceedings of the 20th international conference on Advanced Information Systems Engineering
Getting rid of OR-joins and multiple start events in business process models
Enterprise Information Systems - Challenges and Solutions in Enterprise Computing - 11th International IEEE EDOC Conference (EDOC 2007)
Modularity in Process Models: Review and Effects
BPM '08 Proceedings of the 6th International Conference on Business Process Management
On Measuring Process Model Similarity Based on High-Level Change Operations
ER '08 Proceedings of the 27th International Conference on Conceptual Modeling
On the Formal Semantics of Change Patterns in Process-Aware Information Systems
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
Flexibility in Process-Aware Information Systems
Transactions on Petri Nets and Other Models of Concurrency II
The refined process structure tree
Data & Knowledge Engineering
Configurable Process Models: Experiences from a Municipality Case Study
CAiSE '09 Proceedings of the 21st International Conference on Advanced Information Systems Engineering
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
Discovering Reference Models by Mining Process Variants Using a Heuristic Approach
BPM '09 Proceedings of the 7th International Conference on Business Process Management
Aligning Business Process Models
EDOC '09 Proceedings of the 2009 IEEE International Enterprise Distributed Object Computing Conference (edoc 2009)
Seven process modeling guidelines (7PMG)
Information and Software Technology
OTM '09 Proceedings of the Confederated International Conferences, CoopIS, DOA, IS, and ODBASE 2009 on On the Move to Meaningful Internet Systems: Part I
On the discovery of preferred work practice through business process variants
ER'07 Proceedings of the 26th international conference on Conceptual modeling
Managing process variants as an information resource
BPM'06 Proceedings of the 4th international conference on Business Process Management
The prom framework: a new era in process mining tool support
ICATPN'05 Proceedings of the 26th international conference on Applications and Theory of Petri Nets
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Measures and mechanisms for process monitoring in evolving business networks
Data & Knowledge Engineering
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
Design and management of flexible process variants using templates and rules
Computers in Industry
Applying CVL to business process variability management
Proceedings of the VARiability for You Workshop: Variability Modeling Made Useful for Everyone
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During the last years a new generation of process-aware information systems has emerged, which enables process model configurations at buildtime as well as process instance changes during runtime. Respective model adaptations result in a large number of model variants that are derived from the same process model, but slightly differ in structure. Generally, such model variants are expensive to configure and maintain. In this paper we address two scenarios for learning from process model adaptations and for discovering a reference model out of which the variants can be configured with minimum efforts. The first one is characterized by a reference process model and a collection of related process variants. The goal is to improve the original reference process model such that it fits better to the variant models. The second scenario comprises a collection of process variants, while the original reference model is unknown; i.e., the goal is to ''merge'' these variants into a new reference process model. We suggest two algorithms that are applicable in both scenarios, but have their pros and cons. We provide a systematic comparison of the two algorithms and further contrast them with conventional process mining techniques. Comparison results indicate good performance of our algorithms and also show that specific techniques are needed for learning from process configurations and adaptations. Finally, we provide results from a case study in automotive industry in which we successfully applied our algorithms.