Study level


Master of Philosophy


Vacation research experience scheme


Science and Engineering Faculty

School of Information Systems

Topic status

We're looking for students to study this topic.


Dr Chun Ouyang
Senior Lecturer
Division / Faculty
Science and Engineering Faculty


Process analytics, often known as process mining, mainly involves data-driven analysis of business processes using the large amount of event log data captured by information systems in organisations.

Various analytical techniques help extract insights about the actual business processes with the ultimate goal of process improvement.

Within a broad domain of data analytics, process analytics is faced with a main challenge of information overloading. In the ‘big data’ era we often encounter with the problem that there is too much data to analyse.

Without a clear objective for analysis, extracting the relevant data, analysing data, and interpreting the results can be not only time consuming but also unfruitful.

Hence, we're introducing a new concept called ‘guided process analytics’ to refer to data-driven process analysis initiatives that are motivated by and aim for recommendations of specific process improvement actions.

Research activities

The research activities led by this research initiative are of an explorative nature and can be scoped to cater for your study level.

You'll start from a literature review on novel process analytics approaches and techniques and how they are used to inform process improvement recommendations.


Upon conclusion of this research, we expect to have:

  • new methods and techniques for querying process event logs to retrieve, aggregate, and/or infer relevant and reliable data for process analysis.
  • a ‘tool box’ of data-driven process analysis capabilities motivated by and to inform actionable recommendations for process improvement.
  • systematic approach for generating log requirements to guide data curation for continuous and automated process analytics.

Skills and experience

To be considered for this research project, you should have:

  • knowledge of data mining
  • familiarity with the fields of process analytics and redesign
  • reasonable writing skills
  • problem solving and critical thinking capabilities
  • programming skills, preferably in Python and Java.


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

Annual scholarship round



Contact the supervisor for more information.