Graphs, Sets and Covers; Seeing What Has Always Been There: Finding Common Ground Between BIM Applications and their Users
Bermek, Mehmet Sinan
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Building Information Models and Process Diagrams rely on data modeling types that can vary. By definition, information contained in the very common Relational Model Databases (RMDB) can be contained in the GDBs; by expressing relations as tuples enriched with attributes. Also, other Data Modeling paradigms more specifically explored in the Architecture, Engineering, and Construction (AEC) realm, such as (Extended) Entity Relationship Models (EER), Object Role Models (ORM) are already structured as networks. This makes their direct transfer to GDBs possible, while maintaining functionalities such as “attribute sets” in querying the resulting structures through clustering. Graph Databases (GDB) are database architectures structured to permit network analysis methods on structured data. These databases are built using graph structures comprised of Nodes, Edges, and Properties (Labels or Attributes). These structures can be explored with semantic queries while storing data in an inter-related manner. In the Process Modeling domain, common methods of rigorous communication, such as BPMN or UML–and its more engineering focused subset SysML, derive their validation and semantic execution capabilities thanks to their directed network structure. Again, making it possible to transfer native process model information to GDBs. While these structures can be observed in information models related to building and design practice, in this paper we want to extend the Network Model towards the cognitive processes that are part of design and engineering, to this end World Graph (WG) theory, a metaphysical framework. (Dipert, 1997) WG provides a scaffolding to lay out the interactions between cognitive and motivational states that are part of the decision making. Within this context, attention is given to Small World Networks, which are graphs that can be used to represent frequently encountered problem spaces. A caveat is, that AEC information spaces can present themselves very scattered overall, while tightly clustered within different expertise domains, e.g., paneling dependent on very intricate hardware of many low tolerance components; or material differences in common building methods, such as RC detailing. This is why we believe SWN models can be good candidates for structuring cross domain relationships in a process and object oriented AEC workflow bridging the gap between human cognition and Building Information Models, using rigorous methods, tried and tested in the realm of network science and graph theory. To this end we will demonstrate Graph mappings of design parameters (affordances, objectives, etc.), material properties (ductility, weight, etc.), logistics (order, transportation, etc.), and fabrication methods (shaping, fitting, etc.) tracing a contiguous network between expertise domains, as a proof of concept for developing a common modeling environment between human understanding, communication and storage tools in AEC problem spaces.