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Discovering Object-Centric Causal Nets with Edge Abstraction.

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Title: Discovering Object-Centric Causal Nets with Edge Abstraction.
Authors: de Moura Figueiredo, Ednira1 edfi6431@student.su.se, Jalali, Amin1 aj@dsv.su.se
Source: Complex Systems Informatics & Modeling Quarterly. Dec2025/Jan2026, Issue 45, p20-42. 23p.
Subjects: Causal models, Process mining, Python programming language, Workflow management
Abstract: Object-centric process mining (OCPM) is an emerging research area that aims to analyze processes involving multiple object types (for instance, orders, items, and deliveries in an order-handling process) with complex intertwined relations captured in a richer format than traditional event logs. The richness of these data, as represented in the Object-Centric Event Log (OCEL) standard, often causes existing discovery algorithms to generate models overloaded with information, exceeding the cognitive limits of users, and reducing their practical usefulness. To address this challenge, we introduce Object-Centric Causal Nets (OCCN) together with an edge-abstraction technique that simplifies the discovered model by merging similar flows across object types. While OCCN provides native support for concurrency and choice, the edge abstraction is essential for reducing visual clutter and producing simpler yet expressive models. A Python implementation is provided, and a comparative evaluation against Object-Centric Petri Nets and Object-Centric Directly-Follows Graphs shows that OCCN with edge abstraction yields models that are easier to understand and more effective in enabling users to identify workflow patterns. [ABSTRACT FROM AUTHOR]
Copyright of Complex Systems Informatics & Modeling Quarterly is the property of RTU Publishing House and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="JN" term="%22Complex+Systems+Informatics+%26+Modeling+Quarterly%22">Complex Systems Informatics & Modeling Quarterly</searchLink>. Dec2025/Jan2026, Issue 45, p20-42. 23p.
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  Data: Object-centric process mining (OCPM) is an emerging research area that aims to analyze processes involving multiple object types (for instance, orders, items, and deliveries in an order-handling process) with complex intertwined relations captured in a richer format than traditional event logs. The richness of these data, as represented in the Object-Centric Event Log (OCEL) standard, often causes existing discovery algorithms to generate models overloaded with information, exceeding the cognitive limits of users, and reducing their practical usefulness. To address this challenge, we introduce Object-Centric Causal Nets (OCCN) together with an edge-abstraction technique that simplifies the discovered model by merging similar flows across object types. While OCCN provides native support for concurrency and choice, the edge abstraction is essential for reducing visual clutter and producing simpler yet expressive models. A Python implementation is provided, and a comparative evaluation against Object-Centric Petri Nets and Object-Centric Directly-Follows Graphs shows that OCCN with edge abstraction yields models that are easier to understand and more effective in enabling users to identify workflow patterns. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Complex Systems Informatics & Modeling Quarterly is the property of RTU Publishing House and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Python programming language
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              Text: Dec2025/Jan2026
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