The Experts below are selected from a list of 5070 Experts worldwide ranked by ideXlab platform
L I Sun - One of the best experts on this subject based on the ideXlab platform.
-
etude sur la definition selon le modele Schema Extension
Canadian Social Science, 2021Co-Authors: L I SunAbstract:Cet article presente la pratique de definition qui vise a la fois d’extraire les traits semantiques en communs que partagent les mots dans la meme categorie semantique, et a mieux eclaircir les caracteristiques individuelles de chaque mot. L’objet de cet article est de presenter la definition de certains mots francais en s’appuyant sur le modele Schema-Extension, afin de favoriser la competence de comprehension et de production des apprenants chinois, pour qu’ils puissent differencier les nuances des mots, les maitriser et utiliser dans un contexte adequat, de facon correcte et autonome.
Jakob Beetz - One of the best experts on this subject based on the ideXlab platform.
-
An IFC Schema Extension and binary serialization format to efficiently integrate point cloud data into building models
Advanced Engineering Informatics, 2017Co-Authors: Thomas Krijnen, Jakob BeetzAbstract:Abstract In this paper we suggest an Extension to the Industry Foundation Classes (IFC) model to integrate point cloud datasets. The proposal includes a Schema Extension to the core model allowing the storage of points, either as Cartesian coordinates, points in parametric space of associated building element surfaces or as discrete height fields projected as grids onto building elements. To handle the considerable amounts of data generated in the process of scanning building structures, we present intelligent compression approaches combined with the Hierarchical Data Format (HDF) as an efficient serialization and an alternative to clear text encoded ISO 10303 part 21 files. Based on prototypical implementations we show results of various serialization options and their impacts on storage efficiency. In this proposal the deepened semantic relationships have been favoured over compression ratios. Nevertheless, with various near-lossless layers of compression and binary serialization applied, a compression ratio of up to 67.7% is obtained for a building model with integrated point clouds, compared to the raw source data. The binary serialization is able to handle hundreds of millions of points, out of which specific spatial and semantic subsets can rapidly be extracted due to the containerized hierarchical storage model and association to building elements. The authors advocate the use of binary storage for sizeable point cloud scans, but also show how especially the grid discretization can result into usable points cloud segments embedded into text-based IFC models.
Jiri Safarik - One of the best experts on this subject based on the ideXlab platform.
-
transformation of relational databases to transaction time temporal databases
Engineering of Computer-Based Systems, 2011Co-Authors: Jiri SafarikAbstract:In recent years, versioning has emerged as a very popular operating system feature for file systems. Some of these systems can provide user friendly âgo back in timeâ feature, where the user can see exactly how file system looked on the dates he specifies. The advantages of these techniques are however, not present or supported by most relational database systems used worldwide. Using versioning in relational databases we can decrease the complexity of information systems, provide new functionality, increase the security and usually decrease the administration costs. In this paper we present an approach to transformation of the snapshot relational database to temporal database with tuple time-stamping. The transformation is achieved by automatic database Schema Extension and modification. Information system which uses the transformed database Schema remain intact, or requires minimal modifications. The automatic transformation can be applied on PostgreSQL relational databases, however similar or same transformations can be used in other relational databases.
Thomas Krijnen - One of the best experts on this subject based on the ideXlab platform.
-
An IFC Schema Extension and binary serialization format to efficiently integrate point cloud data into building models
Advanced Engineering Informatics, 2017Co-Authors: Thomas Krijnen, Jakob BeetzAbstract:Abstract In this paper we suggest an Extension to the Industry Foundation Classes (IFC) model to integrate point cloud datasets. The proposal includes a Schema Extension to the core model allowing the storage of points, either as Cartesian coordinates, points in parametric space of associated building element surfaces or as discrete height fields projected as grids onto building elements. To handle the considerable amounts of data generated in the process of scanning building structures, we present intelligent compression approaches combined with the Hierarchical Data Format (HDF) as an efficient serialization and an alternative to clear text encoded ISO 10303 part 21 files. Based on prototypical implementations we show results of various serialization options and their impacts on storage efficiency. In this proposal the deepened semantic relationships have been favoured over compression ratios. Nevertheless, with various near-lossless layers of compression and binary serialization applied, a compression ratio of up to 67.7% is obtained for a building model with integrated point clouds, compared to the raw source data. The binary serialization is able to handle hundreds of millions of points, out of which specific spatial and semantic subsets can rapidly be extracted due to the containerized hierarchical storage model and association to building elements. The authors advocate the use of binary storage for sizeable point cloud scans, but also show how especially the grid discretization can result into usable points cloud segments embedded into text-based IFC models.
Scalas A - One of the best experts on this subject based on the ideXlab platform.
-
TinyKB: a dynamic class/object store and ORM for Python
2011Co-Authors: Scalas AAbstract:TinyKB is a Python module for defining and handling object-oriented knowledge bases composed by cross-referencing classes and objects stored in a SQL DBMS. It is also a “dynamic object-relational mapper” in two senses: the knowl- edge base elements are dynamically mapped into autogenerated Python classes and objects, and the SQL Schema is dynamically extended according to class definitions. This approach departs from other ORMs, which usually expect SQL-mapped Python classes to be statically defined in the source code, and provide little or no support for run-time SQL Schema Extension. TinyKB uses Python dynamic features to provide an easy-to-use API for querying and han- dling the knowledge base, while at the same time ensuring that the underlying SQL Schema is normalized, well-typed and data-centric (thus leveraging the DBMS capabilities for data consistency and integrity)
