"Nothing has such power to broaden the mind as the ability to investigate systematically and truly all that comes under thy observation in life."
- Marcus Aurelius -
Current research projects
2021 - 2022
Web-based application for real-time progress data visualisation with BIM 4D in IBS construction
Student: Tan Kwan Yee
Supervisor: Dr Jing Ying Wong
Centralized real-time tracking of IBS-related construction progress is a daunting task for stakeholders on a construction project.
2020 - Date
An Adaptive Data Processor for the De-silo-fication of Mobility-as-a-Service
Student: Shams Ghazy
Main Supervisor: Dr. Jing Ying Wong
Co-Supervisor: Mr. Yu Hoe Tang
Co-Supervisor: Prof. Andy Chan
External Co-Supervisor: Dr. Pieter Colpaert
In the transportation domain, each mobility service generates data. The data is commonly stored in a certain
structure, with inherent semantics that describe the data, and different access rules. Mobility-as-a-Service (MaaS) aggregates data from heterogeneous sources, not only transport data of varying types of mobility, but also factors affecting the success of a user’s trip.
Every time a MaaS provider integrates a transport operator, they are expected to merge their data into the MaaS ecosystem. This process is not standardized as operators commonly follow different standards better suited for their own services and business rules. Consequently, the MaaS provider needs to configure their services to talk to several custom APIs, resulting in an unscalable and inefficient process, hindering the progress of MaaS.
Currently, there exists a few attempts in standardizing this process such as the TOMP-API, however, the process remains highly dependent on the bilateral agreement a MaaS provider forms with an operator, and hence lacks modularity and adaptability for reuse with the next operator. In addition, there is no clear method for an equitable sharing of user data, precipitating a concern for the operators to take part in a MaaS ecosystem in the first place.
This research proposes the use of Semantic Web Technologies as a solution to formulate an adaptive and interoperable data exchange process between MaaS stakeholders taking into account their heterogeneous interoperability requirements.
2018 - Date
A framework for Knowledge Representation Learning on graph-based building control data
Student: Kevin Luwemba Mugumya
Main supervisor: Dr Jing Ying Wong
Co-supervisor: Prof Andy Chan
As soon as a building is commissioned, a chain of events is set into motion to ensure proper functionality of its systems and that operational efficiency targets are met in compliance with set regulations. This process is called Facility Management (FM) and just like any other stage of a building’s life-cycle, FM is a heavily data-driven process that involves several building systems and multi-disciplinary stakeholders constantly exchanging disintegrated and scattered datasets.
Dealing with siloed data is challenging and to intuitively visualize this, consider the following questions in a building's context.
How much of the data and information used to manage a building is dispersed across its different systems, zones, components, hard copy documentation or databases?
How many clicks and reports do facility managers have to navigate in the interface of a Building Automation System to find an answer to a single FM problem or get relevant results for a specific search?
How efficient is it to optimize a building's FM process using Machine Learning (ML) models that are trained using disintegrated datasets?