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A Multi-Viewpoint Modeling Framework for Digital Twin Integration and Reuse with LLM-Assisted Compatibility Analysis — arXiv:2608.23115

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.23115
License (as archived): CC0-1.0
Archived: 2026-10-18T08:12:42.401Z
SHA-256 of the archived text: 9ef067b1f9cddc860dc7427c51f56af5aaaa03b45520c5e4dd78ae232d9e75d6
Stable URL of this page: https://metraipolis.com/documento/dc_cd36fcbc048c

How to cite

A Multi-Viewpoint Modeling Framework for Digital Twin Integration and Reuse with LLM-Assisted Compatibility Analysis — arXiv:2608.23115 — arXiv (en), https://arxiv.org/abs/2608.23115 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_cd36fcbc048c (Metraipolis archive dc_cd36fcbc048c, sha256 9ef067b1f9cddc860dc7427c51f56af5aaaa03b45520c5e4dd78ae232d9e75d6)

BibTeX

@misc{dccd36fcbc048c,
  title = {A Multi-Viewpoint Modeling Framework for Digital Twin Integration and Reuse with LLM-Assisted Compatibility Analysis — arXiv:2608.23115},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.23115},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_cd36fcbc048c; sha256 9ef067b1f9cddc860dc7427c51f56af5aaaa03b45520c5e4dd78ae232d9e75d6},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.23115. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Digital Twin (DT) ecosystems integrate heterogeneous computational models to represent complex systems under evolving, purpose-specific objectives. Systematic reuse of existing high-quality models and datasets is essential for scalable DT development, yet is constrained by heterogeneity in semantic intent, data structures, behavioral interfaces, and execution environments. As a result, integration becomes a cross-model, cross-view consistency problem that is hard to predict, quantify, and compare across design choices. Existing standards and integration platforms address these concerns separately, offering limited support for structured, purpose-aware compatibility assessment and early feasibility analysis when models are reused under new DT objectives. This paper introduces a multi-viewpoint integration modeling framework grounded in the Reference Model of Open Distributed Processing (RM-ODP). The framework structures integration-relevant knowledge across domain, information, computational, engineering, and technology viewpoints, representing cross-view dependencies as explicit, machine-actionable metadata. It comprises (i) a viewpoint-structured Model Metamodel for systematic model description and discovery, and (ii) a pattern-aware Mismatch Detector that operationalizes cross-view compatibility constraints via integration patterns, combining deterministic rule generation with Large Language Model (LLM)-assisted reasoning. This enables systematic identification of semantic, informational, and runtime inconsistencies and supports reasoning about integration feasibility and effort before implementation. Expert validation and an environmental modeling case study show that the approach enables structured compatibility reasoning, improves transparency of integration assumptions, strengthens cross-view interoperability, and supports scalable reuse in heterogeneous DT ecosystems.

Tema degli esploratori: calcolo e infrastrutture (retro-catalogo arXiv: annunci del 2026-08-25 in cs.SE).
Categorie arXiv: Software Engineering (cs.SE).
Pagina del lavoro: https://arxiv.org/abs/2608.23115 — 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 2026-10-18T08:07:33.016Z dalla pagina https://arxiv.org/catchup/cs.SE/2026-08-25?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).