Description

Book Synopsis

.- FCM.
.- Logical Foundations of Conceptual Modelling for Relational and SQL Databases - an introduction.
.- Disentangling the schema turn: Restoring the information base to conceptual modelling.
.- Representing Autopoiesis and Scanning the Environment in Fractal Enterprise Model.
.- Towards a fundamental theory of modeling discrete systems.
.- Towards Specifying ”Model”.
.- CMLS.
.- A conceptual model for discovering implicit temporal knowledge in clinical data.
.- LLM4Modeling.
.- Vibe Modeling: Challenges and Opportunities.
.- Model Deepening with Large Language Models: Insights from Exploratory Studies with ChatGPT.
.- From ConOps to an Operational Domain Model: Harnessing LLMs for Conceptual Model Design.
.- Towards diagram-based data model generation with LLMs.
.- OntoCom.
.- Unpacking Trust: An Ontological Framework for Information Trustworthiness in Decision-Making.
.- QUAMES.
.- Towards Model-Driven Testing for Assuring the Quality of Large Language Models.
.- Prompt-Guided Evaluation of GPT-4o, Gemini and DeepSeek on UML-to-Java Code Generation.

Advances in Conceptual Modeling

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    A Paperback by Claudenir M. Fonseca

    £58.49

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    RRP £6,499.00 – you save £6,440.51 (99%)

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      Book details

      Publisher Springer
      Published 11/16/2025
      ISBN-13 9783032086198
      978-3032086198
      ISBN-10 3032086191
      Also in:
      Information theory

      Description

      Book Synopsis

      .- FCM.
      .- Logical Foundations of Conceptual Modelling for Relational and SQL Databases - an introduction.
      .- Disentangling the schema turn: Restoring the information base to conceptual modelling.
      .- Representing Autopoiesis and Scanning the Environment in Fractal Enterprise Model.
      .- Towards a fundamental theory of modeling discrete systems.
      .- Towards Specifying ”Model”.
      .- CMLS.
      .- A conceptual model for discovering implicit temporal knowledge in clinical data.
      .- LLM4Modeling.
      .- Vibe Modeling: Challenges and Opportunities.
      .- Model Deepening with Large Language Models: Insights from Exploratory Studies with ChatGPT.
      .- From ConOps to an Operational Domain Model: Harnessing LLMs for Conceptual Model Design.
      .- Towards diagram-based data model generation with LLMs.
      .- OntoCom.
      .- Unpacking Trust: An Ontological Framework for Information Trustworthiness in Decision-Making.
      .- QUAMES.
      .- Towards Model-Driven Testing for Assuring the Quality of Large Language Models.
      .- Prompt-Guided Evaluation of GPT-4o, Gemini and DeepSeek on UML-to-Java Code Generation.

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