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Learning partial correlation graph for multivariate sensor data and detecting sensor communities in smart buildings

Lookup NU author(s): Dr Xiang XieORCiD, Dr Tejal Shah, Professor Mohamad Kassem, Professor Philip James



This is the authors' accepted manuscript of a conference proceedings (inc. abstract) that has been accepted and is due to be published in its final definitive form by LDAC, 2023.

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The storage and processing of massive time series data collected from smart buildings consume considerablecomputational resources. However, major information redundancy can be found in the smartbuilding data. This paper proposed a partial correlation graph based approach to map the dependenciesamong sensors and detect the sensor communities in which the sensors are strongly “net” correlated.Specifically, the sparse partial correlation estimation method is used to learn the partial correlation graph.The Louvain algorithm is used to detect the communities of sensors by optimising the graph modularity.The case study demonstrates that the proposed method can identify spare sensors in the detected sensorcommunities and thus enhance the computational feasibility of smart building applications.

Publication metadata

Author(s): Xie X, Herrera M, Shah T, Kassem M, James P

Publication type: Conference Proceedings (inc. Abstract)

Publication status: In Press

Conference Name: 11th Linked Data in Architecture and Construction Workshop

Year of Conference: 2023

Acceptance date: 01/05/2023

Date deposited: 03/05/2023

Publisher: LDAC