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Research projects in Information Technology

Displaying 1 - 10 of 13 projects.

Data Lake: Managing and Querying Multi-Modal Data

It is very common that an organization has many data sources; not only their operational databases (e.g. Oracle, Postgres, etc). Other data sources include experimental results stored in csv files, reports in pdf and text formats, images, as well as videos. It is very common data this data is stored in various places, e.g. folder, and very often, it is very hard to locate where they are, when we want to find them. We might not know that the kind of data that has been stored in the past. 

Data storage to enable actionable data via quality care dashboards for clinicians

This PhD project is funded by a DHCRC project "Actionable data for clinicians and external accreditors in support of quality care provision and continuous accreditation". The successful applicant will work as part of a larger research team on this project which includes multiple industry partners.

Supervisor: Dr Michael Wybrow

Smart Charging Strategies for Electric Vehicles in Smart Grids

Short description

The global adoption of electric vehicles (EV) expanded notably over the last decade, created opportunities for grid integration through flexible charging and vehicle to grid. This project aims to develop solutions that will help EV Owners and Fleet Managers with smart charging/discharging decisions and aggregation of EV resources to support the grid.

Project description

Navigation and Point of Search in Road Networks

Modern map-based systems and location-based services rely heavily on the ability to efficiently provide navigation services and the capability to search points of interests (POIs) based on their location or textual information. The aim of this project is to build a next-generation navigation system by addressing limitations in the current systems – such as allowing more meaningful distance measures, modeling uncertainty in data sources and queries, and exploiting rich information from several data sources.

Using Big Spatiotemporal Data for Road Safety

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.

Eco-friendly Road Transportation

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.

Geospatial Data Analysis

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).

Big Data Management and Processing

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

Container Orchestration for Optimised Renewable Energy Use in Clouds

This project aims to develop resource management techniques including scheduling and scaling algorithms for container-based virtual clusters in cloud data centres powered with renewable energy sources. Algorithms are designed to optimise renewable energy use while meeting the Quality of Service requirements of applications running on the cluster. Specifically, the project aims to:

1. Define an architectural framework and principles for renewable energy-aware management of containers in cloud data centres;

Algorithms and Software Systems for Energy Flexibility in Green Data Centre Using Software Defined Networking

Interest is growing in powering data centres by energy generated from renewable sources to reduce high operational cost and carbon footprint. In 2017, Google achieved a major milestone of purchasing 100% renewable energy to match its data centres annual electricity consumption. However, efficient utilisation of renewable energy is a challenging problem due to the variable and intermittent nature of both workload demand and renewable energy supply.