  Buildings Automation Systems (BAS) are ubiquitous in contemporary buildings,
both monitoring building conditions and managing the building system control
points. At present, these controls are prescriptive and pre-determined by the
design team, rather than responsive to actual building performance. These are
further limited by prescribed logic, possess only rudimentary visualizations,
and lack broader system integration capabilities. Advances in machine learning,
edge analytics, data management systems, and Facility Management-enabled
Building Information Models (FM-BIMs) permit a novel approach: cloud-hosted
building management. This paper presents an integration technique for mapping
the data from a building Internet of Things (IoT) sensor network to an FM-BIM.
The sensor data naming convention and timeseries analysis strategies integrated
into the data structure are discussed and presented, including the use of a 3D
nested list to permit timeseries data to be mapped to the FM-BIM and readily
visualized. The developed approach is presented through a case study of an
office living lab consisting of a local sensor network mimicking a BAS, which
streams to a cloud server via a virtual private network connection. The
resultant data structure and key visualizations are presented to demonstrate
the value of this approach, which permits the end-user to select the desired
timeframe for visualization and readily step through the spatio-temporal
building performance data.
