Our engagement with prestigious industry partners and the community has led to production of several research projects across multiple application domains.
DSRG faculty members and researchers lead projects that stretch across our research streams. The discovery of data sciences within big data is a topical issue in industry and academia.
DSRG invites students and companies to explore the opportunities of collaboration. To discuss any potential projects please contact us.
Early detection of worsening asthma aims to predict daily asthma related ED visits and admissions.
The overall objective of this research project is to enable brick and mortar (BaM) retailers to track what their customers are looking at.
A fast input stream of customer's sales transactions needs to be joined with a disk-based Master Data.
Big data in the healthcare industry, patient-generated health data, clinical trial results, and Internet of Things (IoT) data all pour into the system.
An aging, overweight population has shifted the focus from acute care to chronic disease management.
Pattern extraction from low to high quality videos: Towards real-time data fusion (wearable/computing devices).
The world total of mobile subscriptions exceeds 6.2 billion (World Bank, 2013).
Proposing a new framework that senses the level of volatility in the stream and adjusts the learning paradigm to the level of volatility.
Creating a framework for detecting cyber harassment from textual communication data.
Aiming to develop models that would be able to extract location information from short Tweet type blogging texts.
Electrical power systems are vulnerable to fluctuations in frequency due to variations that take place in demand.
Earthquakes are a major hazard and have the potential to cause considerable loss of life and property.
Our work on travel data analytics has focused on GPS-based travel surveys.
Creation of prediction models using medical data.
The project will analyse ESR's forensic casework data to identify patterns in the types, timing and outputs of the forensic science conducted.
The aim of the project was to develop a post discharge application.
Analysing high speed data streams while improving throughput and retaining accuracy levels.