Conceptual Meta Model for Building Information Modeling

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F75081431%3A_____%2F20%3A00002040" target="_blank" >RIV/75081431:_____/20:00002040 - isvavai.cz</a>

Výsledek na webu

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

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. Specialized concepts, attributes and relationships inherit all constraints and derivations of the concepts, attributes, relationships they are specialized from. Concepts can be decomposed in concepts; Instances can be decomposed in other instances. Beside the three abstraction mechanisms we also provide other relation types: • Grouping, like for grouping all concepts, attributes, relations etc. into one data model, • Characterisation, indicating how attributes, relations, constraints and derivations relate to concepts/individuals on concept/instance level, and • The general associations on both concept and individual level.

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. Specialized concepts, attributes and relationships inherit all constraints and derivations of the concepts, attributes, relationships they are specialized from. Concepts can be decomposed in concepts; Instances can be decomposed in other instances. Beside the three abstraction mechanisms we also provide other relation types: • Grouping, like for grouping all concepts, attributes, relations etc. into one data model, • Characterisation, indicating how attributes, relations, constraints and derivations relate to concepts/individuals on concept/instance level, and • The general associations on both concept and individual level.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20101 - Civil engineering

Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

Journal of Physics: Conference Series: 13th International Conference on Computer-Aided Technologies in Applied Mathematics, ICAM 2020

ISBN

—

ISSN

1742-6588

e-ISSN

—

Počet stran výsledku

10

Strana od-do

—

Název nakladatele

IOP Publishing

Místo vydání

Spojené království

Místo konání akce

online

Datum konání akce

7. 9. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—