Supporting the building design process with graph-based methods using centrally coordinated federated databases

Abstract

Abstract Background Technological developments and globalized working processes have transformed the building process. However, current digital semantic building models no longer adequately represent the increasing complexity of modern building projects. The potential of combining agent-based and graph-based methods, for example for energy calculations or spatial research strategies, is not fully exploited. Methods In our system, users search for building floorplans, for example as sources of inspiration, by creating conceptual hand-drawn sketches of building parts on multi-touch devices. The sketch is analyzed and used to query a federated database system comprising a building information model server, the graph database neo4j and the content management system mediaTUM. Users interact with client applications that show and continuously update a list of floorplans by sending queries to a central coordinator service. In the paper, we describe the coordinator that enables our databases to appear as a single smart information system to search for digital information about buildings and visualize their floorplans. The application case comprises search by drawing the initial design idea of a building to find similar floorplans e.g. as source of inspiration. Results Our federated database system is queried using semantic building floorplan fingerprints, which are formalized as graphs and encoded in a common schema, like our AGraphML, to represent spatial configurations and perform graph matching. As graph matching is computationally expensive, the coordinator needs to analyze the queries, separate different fingerprints and metadata and pass it to specialized agents, implemented as microservices, for processing. The returned result sets are combined and the results are visualized. The use of multiple agents facilitates the recombination of the data from the underlying disparate data sources and also serves to support different execution strategies which are chosen using properties of the query and a set of predefined rules. Conclusions To deal with complex and dynamic queries, as well as dynamic results that are updated while some agents are still executing, a caching framework was developed which takes the similarity measures of the graph-based building representation into account when determining query equality.