Access to large amounts of real-world data has long been a barrier to the development and evaluation of analytics applications for the built environment. Open datasets exist, but they are limited in their span (how much data is available) and context (what kind of data is available and how it is described). Evaluation of such analytics is also limited by how the analytics themselves are implemented, often using hard-coded names of building components, points and locations, or unique input data formats.
To advance the methodology for how such analytics are implemented and evaluated, we present Mortar: an open testbed for portable building analytics, currently spanning 90 buildings and containing over 9.1 billion data points. All buildings in the testbed are described using Brick, a recently developed metadata schema, providing rich functional descriptions of building assets and subsystems. We also propose a simple architecture for writing portable analytics applications that are robust to the diversity of buildings and can configure themselves based on context. We demonstrate the utility of Mortar by implementing 11 applications from the literature.
}, year = {2020}, journal = {ACM Transactions on Sensor Networks}, volume = {16}, pages = {1 - 31}, month = {02/2021}, issn = {1550-4859}, url = {https://dl.acm.org/doi/10.1145/3366375http://dl.acm.org/ft_gateway.cfm?id=3366375&ftid=2099774&dwn=1https://dl.acm.org/doi/pdf/10.1145/3366375}, doi = {10.1145/3366375}, language = {eng}, }