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This research aims to enhance process discovery techniques to better model complex workflows and decision logic.

June 12, 2026Process ScienceOpen Access

A discovery technique for expressive yet sound process models

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This research aims to enhance process discovery techniques to better model complex workflows and decision logic.
Introduced POWL 2.0, an extended modeling language with choice graphs for improved decision representation.
Developed a robust inductive discovery algorithm to utilize choice graphs in process mining.
Evaluated multiple mining strategies to differentiate between loops and concurrency.
The new approach demonstrates improved representation of complex behaviors compared to traditional methods.
Scalability and quality guarantees of inductive mining are maintained while addressing decision complexities.
Experimental outcomes confirm the effectiveness of POWL 2.0 in modeling unstructured loops and concurrency.

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Randomized trial explores advanced process modeling techniques, highlighting improvements for complex decision making.

Kourani et al. (2026) studied this question.

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