Research projects in Information Technology

On their own, traffic accidents cause 1.3 million fatalities every year – and improper situational awareness is often a major cause. This project aims to exploit big spatio-temporal data to design intelligent techniques for scheduling and offloading tasks to the cloud and peer vehicles. This will ultimately meet the Quality of Service (QoS) requirements of time-critical road safety applications and increase situational awareness by automatically identifying unsafe road conditions and risky driving behaviors – and sending alerts in real time to affected vehicles.

This project aims to harness big data from ubiquitous smartphone sensors to reduce the impact of road transport on the environment. Specifically, we’ll design novel data modelling and indexing techniques to exploit the data and create a next-generation, eco-friendly navigation system which will significantly reduce greenhouse gas emissions and result in fuel saving. The initiative also aims to study the citywide impact of adapting to eco-friendly navigation on traffic, environment and road safety – therefore supporting urban planning and decision-making.

This project heavily focuses on maps (e.g. GoogleMaps or Open Street Map). We will explore various properties of road networks, including the granularity of road networks, routes and trajectories on road networks, and query processing on road networks. 

A number of inter-disciplinary collaboration exists, including transportation to hospitals, urban sprawl analysis, and geospatial in sustainability (e.g. analysing placement of rubbish bins on streets).

This project investigates 3Vs of Big Data (e.g Volume, Variety, and Velocity). 

Volume: Due to the exponential increase in data volume, it is necessary to adopt parallelism techniques to achieve reasonable query response time. The main focus will be on parallel query processing, which is the main driver for Big Data processing

A large part of modern life is lived indoors such as in homes, offices, shopping malls, universities, libraries and airports. However, almost all of the existing location-based services (LBS) have been designed only for outdoor space. This is mainly because the global positioning system (GPS) and other positioning technologies cannot accurately identify the locations in indoor venues.

This project aims to design effective and intelligent search techniques for large scale social network data. The project expects to advance existing social network search systems in three unique aspects: utilizing the geographical locations of queries and social network data to provide more relevant results; acknowledging and handling inherent uncertainties in the data; and exploiting knowledge graphs to produce intelligent search results. Expected outcomes of this project include a next-generation social network search system and enhanced international collaborations.