Responsible Analytics of Process Data

Study level

PhD

Master of Philosophy

Honours

Vacation research experience scheme

Faculty/Lead unit

Science and Engineering Faculty

School of Information Systems

Topic status

We're looking for students to study this topic.

Supervisors

Professor Moe Thandar Wynn

Position: Professor
Division / Faculty: Science and Engineering Faculty

Overview

Technological advances in the field of data science empowers organisations to become ‘data-driven’ by applying new techniques to analyse large amounts of data. The potential benefits include a better understanding of business performance and more-informed decision making for business growth. However, a key roadblock to this vision is the lack of transparency surrounding the quality of data.

Process mining is a specialised form of data-driven process analytics where process data, collated from the different IT systems typically available in organisations, is analysed to uncover the real behaviour and performance of business operations. This technique has been applied in over 100 organisations worldwide and, in Australia, have been used to analyse the behaviour of processes in the healthcare, insurance, retail and telecommunication domains.

The extent to which the outcomes from process mining can be relied upon for insights is directly related to the quality of the input. Most currently available process mining techniques analyse the input data without taking into account how the recorded data has been manipulated or pre-processed beforehand.

Combined with the manual manner in which the original data is manipulated, it is impossible to keep track of the impact on the analysis outcomes of the original data quality and subsequent data manipulations. Therefore, new process mining techniques that can discover reliable process insights are required.

Research activities

Prospective students will work closely with a team of researchers from the Business Process Management discipline to address key research challenges identified in this research topic.

Depending on the nature of the research study being undertaken, the scope of the research project will be adjusted.

As a VRES student you will assist the research team by analyzing real-life data sets, evaluating research software prototypes and conducting user studies with industry partners.

As a research student (Honours, Masters or PhD) you may develop algorithms that can compute data quality metrics for various quality dimensions and generate quality-annotated event logs from industry data sets.

Alternatively, you will design and develop algorithms related to process discovery, process conformance and process performance analysis to utilise knowledge about the quality of an event log. Empirical evaluation will then take place via user studies with industry partners.

Outcomes

After a literature review on data mining and process mining techniques, you will participate in different research tasks depending on your background and chosen topic.

Research project on process data provenance

You will carry out research on patterns-based detection and remediation of data imperfections in an event log.

Your involvement will include:

designing data quality detection and remediation algorithms
developing algorithms that can compute data quality metrics for various quality dimensions and generate quality-annotated event logs from industry data sets,
validating proposed data quality approaches via user studies with Australian industry partners in insurance and banking domains
implementing these techniques in open-source software, e.g. the ProM platform
conducting the evaluation of the resulting framework with synthetic and real-world logs.

Research project on quality-informed process mining

You will carry out research on the design, implementation and evaluation of new algorithms for quality-informed process mining.

Your involvement will include:

designing and developing algorithms related to process discovery, process conformance and process performance analysis which utilise
developing new techniques that make transparent the impact of remediation actions on the analysis results
implementing these techniques in open-source software, e.g. the ProM platform
conducting the evaluation of the resulting framework with synthetic and real-world logs in conjunction with industry partners.

Skills and experience

You should have a strong technical background in computer science, information technology, data science or equivalent.

You should have experience programming in Java and have an interest in applied research with industry partners.

Scholarships

You may be able to apply for a research scholarship in our annual scholarship round.

Annual scholarship round

Keywords

Contact

Contact the supervisor for more information.