A Discovery Technique for Expressive Yet Sound Process Models

A Discovery Technique for Expressive Yet Sound Process Models | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Discovery Technique for Expressive Yet Sound Process Models Humam Kourani, Gyunam Park, Wil M.P. van der Aalst This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8424952/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Process discovery enables organizations to analyze and improve their operations by automatically deriving process models from event logs. While the Inductive Mining framework is widely adopted for ensuring model soundness, its strict block-structured nature often fails to capture the true complexity of real-world control flows. Recent advancements, such as the Partially Ordered Workflow Language (POWL), have relaxed these constraints for concurrency, yet a significant gap remains in effectively modeling complex decision logic and unstructured loops. We bridge this gap by introducing POWL 2.0, an extended modeling language that integrates choice graphs to represent non-block-structured decisions and cyclic flows within a hierarchical framework. In this paper, we present a robust inductive discovery algorithm that leverages POWL 2.0, and we explore several mining strategies for choice graphs. These strategies range in strictness to resolve the ambiguity between genuine loops and hidden concurrency. Our experimental evaluation demonstrates that the proposed approach captures complex behaviors without compromising the scalability and quality guarantees of the Inductive Mining framework. process discovery process modeling Inductive Miner unstructured loops Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 19 Feb, 2026 Reviews received at journal 19 Feb, 2026 Reviews received at journal 22 Jan, 2026 Reviewers agreed at journal 11 Jan, 2026 Reviewers agreed at journal 10 Jan, 2026 Reviewers invited by journal 08 Jan, 2026 Editor assigned by journal 06 Jan, 2026 Submission checks completed at journal 23 Dec, 2025 First submitted to journal 22 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8424952","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":572796125,"identity":"a22f77dd-9efd-4f5a-94ea-2cdea8d656fa","order_by":0,"name":"Humam 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