Cortado

An Interactive Tool for Data-Driven Process Discovery and Modeling

Responsive image

Overview Functionality

Import event logs .xes and initial process models .ptml
Visually explore event logs with the variant explorer
Discover initial process models from user-selected process behavior
Incrementally extend process models by user-selected process behavior

Manually edit process models under construction any time
Export discovered process models as .ptml or .pnml files
Temporal performance analysis, both model-based and model-independent

Download

Cortado is free of charge for non-commercial, academic and evaluation purposes. By downloading Cortado you agree to the end user license agreement (EULA)


Cortado 1.9.0 (2023-01-23)

Built on Windows 10
(Successfully tested also on Windows 11)


Cortado 1.9.0 (2023-01-23)

Built on Ubuntu 20.04.3 LTS


Cortado 1.9.0 (2023-01-23)

Built on macOS 12 (Apple Silicon)
(Intel processors are not supported)
Troubleshooting: "Cortado.app" is damaged and can't be opened...

Latest Release: Version 1.9.0 (2023-01-23)
Loading ...

Publications

Cortado - An Interactive Tool for Data-Driven Process Discovery and Modeling
Presented at the 42nd International Conference on Application and Theory of Petri Nets and Concurrency (2021)

Download Preprint

The paper covers...

  • concept of Cortado
  • overview on incremental process discovery
  • detailed overview on Cortado's functionality
  • implementation of Cortado

Accessing the paper

Cite Cortado

Schuster D., van Zelst S.J., van der Aalst W.M.P. (2021) Cortado—An Interactive Tool for Data-Driven Process Discovery and Modeling. In: Application and Theory of Petri Nets and Concurrency. PETRI NETS 2021. Lecture Notes in Computer Science, vol 12734. Springer, Cham. https://doi.org/10.1007/978-3-030-76983-3_23

.bibtex


Further publications on algorithmic aspects and individual features

Incremental Discovery of Hierarchical Process Models
Schuster, D., van Zelst, S.J., van der Aalst, W.M.P. (2020). Incremental Discovery of Hierarchical Process Models. In: Research Challenges in Information Science. RCIS 2020. Lecture Notes in Business Information Processing, vol 385. Springer, Cham.
Freezing Sub-Models During Incremental Process Discovery
Schuster, D., van Zelst, S.J., van der Aalst, W.M.P. (2021). Freezing Sub-models During Incremental Process Discovery. In: Conceptual Modeling. ER 2021. Lecture Notes in Computer Science, vol 13011. Springer, Cham.
Sub-Model Freezing During Incremental Process Discovery in Cortado (Extended Abstract)
Schuster, D., van Zelst, S.J., van der Aalst, W.M.P. (2021). Sub-Model Freezing During Incremental Process Discovery in Cortado (Extended Abstract). In: Proceedings of the ICPM Doctoral Consortium and Demo Track 2021 co-located with 10th International Conference on Process Mining (ICPM 2021). CEUR Workshop Proceedings, vol 3098.
Visualizing Trace Variants From Partially Ordered Event Data
Schuster, D., Schade, L., van Zelst, S.J., van der Aalst, W.M.P. (2022). Visualizing Trace Variants from Partially Ordered Event Data. In: Process Mining Workshops. ICPM 2021. Lecture Notes in Business Information Processing, vol 433. Springer, Cham.
Temporal Performance Analysis for Block-Structured Process Models in Cortado
Schuster, D., Schade, L., van Zelst, S.J., van der Aalst, W.M.P. (2022). Temporal Performance Analysis for Block-Structured Process Models in Cortado. In: Intelligent Information Systems. CAiSE 2022. Lecture Notes in Business Information Processing, vol 452. Springer, Cham. https://doi.org/10.1007/978-3-031-07481-3_13

Contact

Get in touch if you have any questions or custom request

email