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Conceptual Meta Model for Building Information Modeling

Identifikátory výsledku

  • Kód výsledku v IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F20%3A00348625" target="_blank" >RIV/68407700:21110/20:00348625 - isvavai.cz</a>

  • Výsledek na webu

    <a href="https://doi.org/10.1088/1742-6596/1680/1/012037" target="_blank" >https://doi.org/10.1088/1742-6596/1680/1/012037</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1088/1742-6596/1680/1/012037" target="_blank" >10.1088/1742-6596/1680/1/012037</a>

Alternativní jazyky

  • Jazyk výsledku

    angličtina

  • Název v původním jazyce

    Conceptual Meta Model for Building Information Modeling

  • Popis výsledku v původním jazyce

    The first meta-concept reflects a specification for data as a whole. We call this a Data Model. Good examples are ontologies or dictionaries. In addition, we have Data Sets that contain individual data according to the Data Model. The next basic meta-concept inside such a data model is a Concept referring to abstract notions as types of things of interest. Next we have Attributes being able to describe intrinsic characteristics and Relations to describe extrinsic characteristics of concepts. Concepts can be instantiated with Individuals referring to real world things you can or could point at. Such instances get lexical or reference Values for attributes respectively relations. Lexical values can be classified according to some Value Type (like string, decimal, integer, boolean etc.). Concepts can have Constraints restricting the amount of values or the values themselves or both. Also attributes and relations can have restrictions with respect to their source or target concepts (in case of relations) or value type (in case of attributes). Finally we have Derivations for concepts that tell us how new values for attributes or relations can be inferred from existing asserted values. We define three mechanisms as specific relations: 1. Classification (inverse: instantiation), from 'concrete' to 'abstract' 2. Generalization (inverse: specialization), from 'specific' to 'generic' 3. Composition (inverse: decomposition), from 'detailed' to 'global' These three mechanisms generate three hierarchy types namely a typology (of concepts), a taxonomy (of concepts, attributes or relations) and a meronomy (of concepts) respectively. Concepts are themselves instances of 'Concept' and can be instantiated in instances; Value Types are themselves instances of 'Value Type' and can be instantiated in values. Concepts can be specialized in other Concepts; attributes can be specialized in other attributes, and relations can be specialized in other relations.

  • Název v anglickém jazyce

    Conceptual Meta Model for Building Information Modeling

  • Popis výsledku anglicky

    The first meta-concept reflects a specification for data as a whole. We call this a Data Model. Good examples are ontologies or dictionaries. In addition, we have Data Sets that contain individual data according to the Data Model. The next basic meta-concept inside such a data model is a Concept referring to abstract notions as types of things of interest. Next we have Attributes being able to describe intrinsic characteristics and Relations to describe extrinsic characteristics of concepts. Concepts can be instantiated with Individuals referring to real world things you can or could point at. Such instances get lexical or reference Values for attributes respectively relations. Lexical values can be classified according to some Value Type (like string, decimal, integer, boolean etc.). Concepts can have Constraints restricting the amount of values or the values themselves or both. Also attributes and relations can have restrictions with respect to their source or target concepts (in case of relations) or value type (in case of attributes). Finally we have Derivations for concepts that tell us how new values for attributes or relations can be inferred from existing asserted values. We define three mechanisms as specific relations: 1. Classification (inverse: instantiation), from 'concrete' to 'abstract' 2. Generalization (inverse: specialization), from 'specific' to 'generic' 3. Composition (inverse: decomposition), from 'detailed' to 'global' These three mechanisms generate three hierarchy types namely a typology (of concepts), a taxonomy (of concepts, attributes or relations) and a meronomy (of concepts) respectively. Concepts are themselves instances of 'Concept' and can be instantiated in instances; Value Types are themselves instances of 'Value Type' and can be instantiated in values. Concepts can be specialized in other Concepts; attributes can be specialized in other attributes, and relations can be specialized in other relations.

Klasifikace

  • Druh

    J<sub>ost</sub> - Ostatní články v recenzovaných periodicích

  • CEP obor

  • OECD FORD obor

    20101 - Civil engineering

Návaznosti výsledku

  • Projekt

  • Návaznosti

    V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Ostatní

  • Rok uplatnění

    2020

  • Kód důvěrnosti údajů

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Údaje specifické pro druh výsledku

  • Název periodika

    Journal of Physics: Conference Series (JPCS)

  • ISSN

    1742-6588

  • e-ISSN

    1742-6588

  • Svazek periodika

    1680

  • Číslo periodika v rámci svazku

    1680

  • Stát vydavatele periodika

    GB - Spojené království Velké Británie a Severního Irska

  • Počet stran výsledku

    10

  • Strana od-do

    1-10

  • Kód UT WoS článku

  • EID výsledku v databázi Scopus

    2-s2.0-85098597597