Workflow Simulation for Operational Decision Support Using Design, Historic and State Information
BPM '08 Proceedings of the 6th International Conference on Business Process Management
Information Systems
Process-Aware Information Systems: Lessons to Be Learned from Process Mining
Transactions on Petri Nets and Other Models of Concurrency II
Workflow simulation for operational decision support
Data & Knowledge Engineering
Towards a Methodology for Modeling Deontic Protocols Using the Organizational Petri Nets Formalism
KES-AMSTA '09 Proceedings of the Third KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications
Redesigning business processes: a methodology based on simulation and process mining techniques
Knowledge and Information Systems
CHANGEMINER: a solution for discovering IT change templates from past execution traces
IM'09 Proceedings of the 11th IFIP/IEEE international conference on Symposium on Integrated Network Management
Time prediction based on process mining
Information Systems
Workflows to open provenance graphs, round-trip
Future Generation Computer Systems
Mashups and widget orchestration
Proceedings of the International Conference on Management of Emergent Digital EcoSystems
History-Dependent stochastic petri nets
PSI'09 Proceedings of the 7th international Andrei Ershov Memorial conference on Perspectives of Systems Informatics
Start time and duration distribution estimation in semi-structured processes
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Business process mining from e-commerce web logs
BPM'13 Proceedings of the 11th international conference on Business Process Management
Information and Software Technology
Hi-index | 0.00 |
Process-aware information systems typically log events (e.g., in transaction logs or audit trails) related to the actual execution of business processes. Analysis of these execution logs may reveal important knowledge that can help organizations to improve the quality of their services. Starting from a process model, which can be discovered by conventional process mining algorithms, we analyze how data attributes influence the choices made in the process based on past process executions using decision mining, also referred to as decision point analysis. In this paper we describe how the resulting model (including the discovered data dependencies) can be represented as a Colored Petri Net (CPN), and how further perspectives, such as the performance and organizational perspective, can be incorporated. We also present a CPN Tools Export plug-in implemented within the ProM framework. Using this plug-in, simulation models in ProM obtained via a combination of various process mining techniques can be exported to CPN Tools. We believe that the combination of automatic discovery of process models using ProM and the simulation capabilities of CPN Tools offers an innovative way to improve business processes. The discovered process model describes reality better than most hand-crafted simulation models. Moreover, the simulation models are constructed in such a way that it is easy to explore various redesigns.