Brick :Towards a Unified Metadata Schema For Buildings

Bharathan Balaji, Arka Bhattacharya, Gabriel Fierro, Jingkun Gao, Joshua Gluck, Dezhi Hong, Aslak Johansen, Jason Koh, Joern Ploennigs, Yuvraj Agarwal, Mario Berges, David Culler, Rajesh Gupta, Mikkel Baun Kjærgaard, Mani Srivastava, Kamin Whitehouse

Research output: Chapter in Book/Report/Conference proceedingConference contribution

183 Scopus citations


Commercial buildings have long since been a primary target for applications from a number of areas: from cyber-physical systems to building energy use to improved human interactions in built environments. While technological advances have been made in these areas, such solutions rarely experience widespread adoption due to the lack of a common descriptive schema which would reduce the now-prohibitive cost of porting these applications and systems to different buildings. Recent attempts have sought to address this issue through data standards and metadata schemes, but fail to capture the set of relationships and entities required by real applications. Building upon these works, this paper describes Brick, a uniform schema for representing metadata in buildings. Our schema defines a concrete ontology for sensors, subsystems and relationships among them, which enables portable applications. We demonstrate the completeness and effectiveness of Brick by using it to represent the entire vendor-specific sensor metadata of six diverse buildings across different campuses, comprising 17,700 data points, and running eight complex unmodified applications on these buildings.
Original languageEnglish (US)
Title of host publicationProceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments
Number of pages10
ISBN (Print)9781450342643
StatePublished - Nov 16 2016
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2021-03-29
Acknowledged KAUST grant number(s): OSR-2015-Sensors-2707
Acknowledgements: Our sincere thanks to the following grants for supporting this work - National Science Foundation grants: CPS-1239552, NSF-1636879, IIS-1636916, CSR-1526237, CNS-1526841, NSF-1305362; U.S. Department of Energy grant: DE-EE0006353; King Abdullah University of Science and Technology award: Sensor Innovation Award #OSR-2015-Sensors-2707; Innovation Fund Denmark award: COORDICY(4106-00003B); EU H2020 grant: TOPAs (676760) and support from Intel Corporation.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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