Filter by faculty:

Found 43 matching student topics

Displaying 1–12 of 43 results

Analysis of weather and electricity prices for solar thermal power applications

Concentrating solar power (CSP) is a technology that utilises mirrors (heliostats) to focuses the sun’s rays on a solar receiver to provide heat that can be stored and used for electricity generation.Control and planning of the use of stored energy would greatly benefit from a detailed understanding of the statistical characteristics of:direct solar irradiation (DNI)other weather variables (e.g. dust in air)electricity prices on the real-time market.The aim of this project will be to develop suitable statistical models that will enable …

Study level
Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Science and Engineering Faculty
School
School of Mechanical, Medical and Process Engineering
Research centre(s)

Centre for Clean Energy Technologies and Practices

The interconnected nature of health information systems

The healthcare industry, once recognised as being a technology laggard is now in the midst of a digital transformation. They are now faced with a mass of systems some inter-connected and some disjoint each contributing different data, some of which is conflicting.This makes it challenging for informed decisions to be made by clinicians. It also has the potential to limit deeper insights into medical diagnoses.Thus there is a need to comprehensively understand these systems, the data they generate and how …

Study level
PhD, Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Science and Engineering Faculty
School
School of Information Systems
Research centre(s)

Forecasting disease spread risk based on human movement patterns

This project aims to forecast the risk of infectious disease spread, such as COVID-19 and dengue, based on human movement patterns. We'll use multiple data sources that describe people movement in order to understand individual and population level mobility patterns, and use empirical disease case data to model the effect of movement on the spread of disease.

Study level
PhD, Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Science and Engineering Faculty
School
School of Computer Science
Research centre(s)
Centre for Data Science

Trust in Internet-of-Things with blockchain

Blockchain is an unchangeable, distributed database. which provides trust in data once it is stored on the database. However, in Internet-of-Things (IoT), the data is an observation of physical context and is susceptible to noise, drift, or malicious alterations. Sensors may be even decoupled from their intended context by an attacker, which may compromise the blockchain data and its value for guiding decision.This project aims to develop an innovative approach for pervasive trust in IoT, underpinned by blockchain. The research …

Study level
PhD, Honours, Vacation research experience scheme
Faculty
Science and Engineering Faculty
School
School of Computer Science
Research centre(s)
Centre for Data Science

Data quality issues in NoSQL databases

In the era of big data, NoSQL (not only structured query language) databases are gaining significance. NoSQL databases offer high performance, availability, scalability, and storage. They have flexible and simple architecture. These characteristics make them a popular candidate for big data analytics.Analysis of big data enables organisations to make informed decisions and decide on strategic moves. However, these decisions can be detrimental to the progress of organisations if the quality of input data is poor, bringing forth the significance of …

Study level
Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Science and Engineering Faculty
School
School of Information Systems
Research centre(s)
Centre for Data Science

Preventing data quality problems in process mining

Process mining is an established approach to analysis of processes based on data found in event logs.Given its use of event data as a starting point, recommendations for business process improvement are evidence-based. As such, there's an increased interest in process mining, both in academia and in industry.Process mining results heavily depend on the quality of event log data. As the saying goes: garbage in, garbage out.While approaches exists that try and repair problematic data, this project will focus on …

Study level
PhD
Faculty
Science and Engineering Faculty
School
School of Information Systems
Research centre(s)
Centre for Data Science

Characterising distant galaxies by way of spectral energy distribution analysis

The analysis of galaxies found throughout cosmic time provides us with the means to probe the underlying characteristics of the known universe.Over the last few decades, new observations of galaxies with various ground and space-based telescopes have provided us with an abundance of multi-wavelength data.By coupling these observations with theory, spectral energy distribution analysis allows us to derive the intrinsic properties of distant galaxies, which provides constraints for various galaxy formation and evolution scenarios.During this project, you will have access …

Study level
Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Science and Engineering Faculty
School
School of Chemistry and Physics
Research centre(s)

Stochastic process mining

Process mining aims to derive information from historical behaviour of processes in organisations through event logs. Business analysts use process mining software to visualise logs and derive information and insights for managers. Ultimately, this information is used to improve processes, which can optimise costs, time and/or the environment. Process mining is an exciting field with lots of opportunity for research and with many successful commercial solutions being offered.Business process models describe what can happen in a process. That is, the …

Study level
PhD, Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Science and Engineering Faculty
School
School of Information Systems
Research centre(s)
Centre for Data Science

Combining process mining with data mining

Process mining aims to obtain insights on business processes from event logs, using algorithms to:automatically discover modelscheck certain rules and regulationsproject performance on process models.Process mining techniques typically only deal with the control flow: that is, which process steps are being executed for a particular case and in what order.Our industry partners are often interested in, and pose questions on, the interplay between process and data. These questions include:Do gold customers get a different treatment than silver customers?Are there behavioural …

Study level
PhD, Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Science and Engineering Faculty
School
School of Information Systems
Research centre(s)
Centre for Data Science

Understanding the mechanisms of improving log quality in different process mining tools

Process mining is a specialised form of data-driven process analytics where process data is analysed to uncover the real behaviour and performance of business operations. This data is usually collated from the different IT systems typically available in organisations.For the data and process analysis to be accurate, the accuracy of data is crucial. This identifies the significance of the quality of logs supplied for analysis.It's the responsibility of the process analyst to identify, assess and remedy the data to reduce …

Study level
PhD, Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Science and Engineering Faculty
School
School of Information Systems
Research centre(s)
Centre for Data Science

Conceptual modelling for data analytics

The project aims to investigate how conceptual modelling can be used to improve understanding of the data and help to select the best approach for predictive analytics.Such a thesis will involve field studies and empirical work to how conceptual modelling can use to ensure that relevant data are collected for the analysis and/or how conceptual modeling can be used to better understanding of the results of such analysis.

Study level
PhD, Master of Philosophy, Honours
Faculty
Science and Engineering Faculty
School
School of Information Systems
Research centre(s)

Explainable AI-enabled predictive analytics for decision making support

Modern predictive analytics, underpinned by AI-enabled learning (such as machine learning, deep learning) techniques, has become a key enabler to the automation of data-driven decision making.In the context of business process management, predictive analytics makes predictions about the future state of a running business process instance. These predictions can include:which task will be carried out nextwhen the task be carried outwho will perform the taskwhen an ongoing process instance will be completewhat the outcome will be upon completion.Machine learning models …

Study level
PhD, Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Science and Engineering Faculty
School
School of Information Systems
Research centre(s)
Centre for Data Science

Page 1 of 4