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OCEL (Object-Centric Event Log) 2.0 Specification

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arxiv 2403.01975 v1 pith:NVOLVNLZ submitted 2024-03-04 cs.DB

Alessandro Berti , Istvan Koren , Jan Niklas Adams , Gyunam Park , Benedikt Knopp , Nina Graves , Majid Rafiei , Lukas Li{ss}
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Leah Tacke Genannt Unterberg Yisong Zhang Christopher Schwanen Marco Pegoraro Wil M.P. van der Aalst
This is my paper · ORCID
classification cs.DB
keywords ocelobject-centricstandardeventexchangeexpressivefirstformat
verification ladder T0 review T1 audit T2 compute T3 formal
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Object-Centric Event Logs (OCELs) form the basis for Object-Centric Process Mining (OCPM). OCEL 1.0 was first released in 2020 and triggered the development of a range of OCPM techniques. OCEL 2.0 forms the new, more expressive standard, allowing for more extensive process analyses while remaining in an easily exchangeable format. In contrast to the first OCEL standard, it can depict changes in objects, provide information on object relationships, and qualify these relationships to other objects or specific events. Compared to XES, it is more expressive, less complicated, and better readable. OCEL 2.0 offers three exchange formats: a relational database (SQLite), XML, and JSON format. This OCEL 2.0 specification document provides an introduction to the standard, its metamodel, and its exchange formats, aimed at practitioners and researchers alike.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 12 citations worldwide. Full citation record

  1. An ER-Model-Based Framework for Case Notion Selection in Object-Centric Processes

    cs.DB 2026-07 conditional novelty 5.0 of 10

    An ER-schema-guided framework defines process cases as connected components of a graph over primary and secondary entity objects, after excluding resource and parent entities.

  2. Leveraging GPT-4o Efficiency for Detecting Rework Anomaly in Business Processes

    cs.LG 2025-02 reject novelty 4.0 of 10

    GPT-4o achieves 74-98% accuracy on synthetic rework anomaly detection depending on prompt type and anomaly distribution, but the prompt examples leak from the test set.

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