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Published in Cornelius Leondes, Optimization Methods for Manufacturing, 2019
Peter Heimann, Bernhard Westfechtel
A configuration object graph represents version-independent structural information in the object plane. The following constraint must hold between a configuration object graph and the corresponding configuration version graphs in the version plane: For each version component (dependency) contained in a configuration version graph, a corresponding object component (dependency) must exist in the configuration object graph. More precisely, each configuration version graph must be mapped by a graph monomorphism into the corresponding configuration object graph. This constraint guarantees that the version plane actually refines the object plane. Furthermore, an injective mapping excludes that multiple versions of some object are contained in one configuration (version consistency). The upper part of Figure 2.4 shows a configuration object graph which satisfies these constraints.
Software design for building model servers: Concurrency aspects
Published in Manuel Martínez, Raimar Scherer, eWork and eBusiness in Architecture, Engineering and Construction, 2020
A building model server’s primary functional requirement in this context is to make an up-to-date object model–agraph of related objects – available to multiple clients (either local or remote) with different data access patterns. Clients should be able to query the model for single objects or subsets of the object graph and to change the state of single objects or the structure of the object graph. Additionally, changes of objects may trigger changes in the physical environment: the model is not merely a view, but an interface to building systems. Older versions of the object model must remain available, i.e. a history of the model must be maintained.
Application of the algorithm of separating graph neural recommendation model in health information system
Published in Journal of Decision Systems, 2021
The contrary idea to the distance framework is said to be as similarity matrix. The components of a closeness lattice measure pairwise likenesses of articles – the more prominent comparability of two items, the more noteworthy the worth of the action. The adjacency matrix of user object graph composed of original relational data can be denoted as A. Based on analysis, to know that A is composed of R and RT and two zero matrices, it is attempted to separate A into two homogeneous networks SU and SV, where SU and SV, respectively, represent the behaviour similarity matrix of users and objects in A. A is an (m +n) * (m +n) matrix and R is an m* n matrix, where m represents the number of users and n represents the number of objects SUij represents the similarity between user i and j.
ALAS: agent-oriented domain-specific language for the development of intelligent distributed non-axiomatic reasoning agents
Published in Enterprise Information Systems, 2018
Dejan Sredojević, Milan Vidaković, Mirjana Ivanović
The main part of the compiler system is the syntax module where we specify both the ALAS grammar and the agent model in a textual form (Figure 3). From a defined grammar, textX automatically creates Arpeggio parser as well as a meta-model that contains the Python classes derived from grammar rules and all the necessary information about the language. Based on the program written in the new language, Arpeggio parser creates Python object graph a.k.a. the model conforming to the meta-model. Parser checks the grammar validity and compatibility model (agent) with meta-model (grammar). If the model corresponds to the meta-model, the console will print messages if model and meta-model are OK; if there is an error, the console will print the error and the exact location of the error.
Evaluating the geometric aspects of integrating BIM data into city models
Published in Journal of Spatial Science, 2020
Jing Sun, Perola Olsson, Helen Eriksson, Lars Harrie
There is a growing interest in the use of BIM data in geospatial datasets with a focus on the life cycle of buildings. Related applications include urban planning, design, construction, operation, and maintenance (Fosu et al. 2015, Song et al. 2017, Ma and Ren 2017, Liu et al. 2017). The conversion of BIM data to GIS data has numerous challenges due to the differences in the reference system, spatial scale, level of granularity, geometric representation methods, storage, and access methods as well as semantic mismatches between BIM and GIS data models (El-Mekawy and Östman 2010, Irizarry et al. 2013, Amirebrahimi et al. 2015, Deng et al. 2016). Several studies have addressed conversion of the geometry. Donkers et al. (2016) developed an automatic conversion method from IFC to CityGML LOD3. One shortcoming they identified was a lack of information in the BIM data as to which building elements are parts of the exterior shell. Another shortcoming is that IFC and CityGML use different geometric representations, i.e. CSG and B-Rep, respectively (Abdul-Rahman and Pilouk 2007). For the semantic part of the conversion, IFC is flexible in terms of how different object types are related to each other; whereas this is more regulated in CityGML, where there are strict rules of how windows can be connected to rooms (Donkers et al. 2016). Stouffs et al. (2018) attempted to obtain a lossless conversion from IFC to CityGML and extended the CityGML model with more classes and attributes, which were implemented in a CityGML application domain extension (ADE). They adopted a graph matching approach and defined triple graph grammars to determine the relationship between the IFC object graph and the CityGML ADE object graph.