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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 work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


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

Editor(s): Walter Terkaj, María Poveda-Villalón and Pieter Pauwels

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: Proceedings LDAC2023 - 11th Linked Data in Architecture and Construction

Year of Conference: 2023

Pages: 201-211

Online publication date: 01/02/2024

Acceptance date: 01/05/2023

Date deposited: 03/05/2023

ISSN: 1613-0073

Publisher: CEUR Workshop Proceedings