Utilising Latent Data in Smart Buildings: A Process Model to Collect, Analyse and Make Building Data Accessible for Smart Industries

Zohreh Pourzolfaghar, Markus Helfert
2017 Position Papers of the 2017 Federated Conference on Computer Science and Information Systems  
Smart buildings are embedded with large amounts of latent data from different sources, e.g. IoT devices, sensors, and the like. Integration of this latent data with the buildings information can highly impact efficiency services provided by various industries such as facility management companies, utility companies, smart commerce, and so forth. To enable the integration of buildings information, diverse technologies such as Building Information Modelling (BIM) have been developed and changed
more » ... e traditional approaches. Notwithstanding a plethora of research in this area, potential users of this information such as facility management companies are still unable to fully benefit from the building information. This is due to this fact that various information and data have been heterogeneously scattered across various sources. To overcome this challenge, this research follows the design science approach to propose a process model to address facility management concerns in terms of the ability to access the combination of building information with live data captured from various sources. The presented process model is introduced thoroughly by explaining the required steps to collect and integrate this information with the live data. The Artifact evaluation of the process model was undertaken via the employment of a focus group session with the construction professionals, the IoT experts, and the data analysts. Also, this paper elaborates on two industrial use-cases to demonstrate how having access to the building information effectively affects the other industries. The outcome of this research provides an open access to the integrated building information and live data for diverse range of users.
doi:10.15439/2017f545 dblp:conf/fedcsis/PourzolfagharH17 fatcat:ku2pyzxlijfuvbutldq43lrkni