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Extending the MM-AR Metamodeling Platform with Process Mining Capabilities — arXiv:2611.05176

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.05176
License (as archived): CC0-1.0
Archived: 2026-11-04T21:50:42.859Z
SHA-256 of the archived text: a746f914683436f0ec0bd175e71b9deb7db5845a621c230f450e1b201ffaa08b
Stable URL of this page: https://metraipolis.com/documento/dc_cd75ffe510a2

How to cite

Extending the MM-AR Metamodeling Platform with Process Mining Capabilities — arXiv:2611.05176 — arXiv (en), https://arxiv.org/abs/2611.05176 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_cd75ffe510a2 (Metraipolis archive dc_cd75ffe510a2, sha256 a746f914683436f0ec0bd175e71b9deb7db5845a621c230f450e1b201ffaa08b)

BibTeX

@misc{dccd75ffe510a2,
  title = {Extending the MM-AR Metamodeling Platform with Process Mining Capabilities — arXiv:2611.05176},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.05176},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_cd75ffe510a2; sha256 a746f914683436f0ec0bd175e71b9deb7db5845a621c230f450e1b201ffaa08b},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.05176. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Process mining derives process models from event data recorded by information systems, but its results are usually presented as two-dimensional visualizations that are separate from the environments in which enterprise models are created and maintained. Metamodeling platforms for extended reality (XR), such as MM-AR, allow users to define modeling languages and to display models in 2D and 3D, but they do not provide access to event data or mining algorithms so far. In this paper we therefore present a first proof-of-concept for showing how an XR-based metamodeling platform can be extended with process discovery capabilities without changes to the platform core through a separate Python service based on the open source PM4Py process mining library. We demonstrate the approach using three common discovery algorithms for obtaining Petri nets and BPMN models based on CSV or XES event logs. A technical evaluation using four public event logs shows that the element counts of the created model instances match those of the PM4Py models in all 16 configurations. The integration adds between about 8 and 53 seconds per run, and this overhead grows with the size of the discovered model rather than the size of the event log. Although the prototype does not yet use the third dimension to encode process information, it established the foundations for studying such approaches.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.HC, nuovi lavori del giorno).
Categorie arXiv: Software Engineering (cs.SE) ; Databases (cs.DB); Human-Computer Interaction (cs.HC).
Pagina del lavoro: https://arxiv.org/abs/2611.05176 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-04T21:38:34.508Z dalla pagina https://arxiv.org/list/cs.HC/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).