Handling huge and complex 3D geometries with Semantic Web technology
| Autor: | Sander Bruinenberg, Rizal Sebastian, Peter Bonsma, Anna Elisabetta Ziri, Ernesto Iadanza, Iveta Bonsma, Federico Ferrari, Federica Maietti, Marco Medici, Pedro Martín Lerones |
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| Rok vydání: | 2018 |
| Předmět: |
H-BIM
Computer science 0211 other engineering and technologies Point cloud 02 engineering and technology Ontology (information science) Semantic data model Semantics SEMANTIC WEB CULTURAL HERITAGE BIM RDS TRIPLE STORE NO 3D geometries Semantic Web H-BIM Digital Heritage 3D modelling Minimum bounding box SEMANTIC WEB CULTURAL HERITAGE BIM RDS TRIPLE STORE 11. Sustainability 021105 building & construction 0501 psychology and cognitive sciences Semantic Web Geometric data analysis Information retrieval 05 social sciences 3D modelling 3D geometries Cultural heritage Digital Heritage 050104 developmental & child psychology |
| Zdroj: | IOP Conference Series: Materials Science and Engineering |
| ISSN: | 1757-899X 1757-8981 |
| DOI: | 10.1088/1757-899x/364/1/012041 |
| Popis: | In INCEPTION, a European collaborative research project, a Heritage BIM (H-BIM) ontology is being developed to store all relevant semantic data concerning cultural heritage objects. Similar to other projects dealing with storing semantics, one of the major questions is whether, and if yes, how should geometry be stored using semantic web technology. The INCEPTION cross-disciplinary research consortium chose to allow the storage of all relevant geometric information using semantic web technology. The alternative is to store geometry in a different way, or storing only the aggregated parts of geometry, for example through bounding box representations.The geometry is originally generated by a CAD/BIM system and, as we are dealing with Cultural Heritage, in many cases it is derived from 3D point clouds. These result in a large amount of 3D data to be stored using semantic web technology. A well-known issue is that the performance of databases and inferencing engines for semantic web data drops considerably when the data grows to very large sizes. This paper explains how the performance issues on these large sets of geometric data can be solved while still being able to use the databases, inferencing engines, and the geometric data effectively. |
| Databáze: | OpenAIRE |
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