Development of a Visual Analtyics Methodology for Streaming Data
The velocity aspect of Big Data Analytics stems from recent advances in hardware and software technology to sense, capture process and communicate data. A prominent application of this is the Internet of Things. In recent years there have been considerable developments in data mining algorithms specialising on real-time data (data streams). These algorithms create update and maintain analytics models automatically. However, there has been little work on interactive visualisations tailored for streaming data, even though the combination of intelligent algorithms with visualisations to harvest the power of the human brain could result a powerful visual analytics tool/methodology. This project aims to develop novel methods and algorithms for visual analytics of data streams such as sensor data of smart homes, the Internet of Things, etc.
For enquiries please contact [Email Address Removed]. Keywords: Big Data Analytics, Visual Analytics, Data Stream Mining, Internet of Things.
There is no funding associated with the PhD study. However applicants are encouraged to apply for funding from any funding bodies.
First degree in computer science, physics, engineering, and mathematics with 2:1 or abov. MSc degree in the relevant subject areas is desired.