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

Study level


Vacation research experience scheme

Topic status

We're looking for students to study this topic.


Dr Kanika Goel
Associate Lecturer
Division / Faculty
Science and Engineering Faculty


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.

For the data and process analysis to be accurate, the accuracy of data is crucial. The adds significance to the quality of logs supplied for analysis. It is the responsibility of the process analyst to identify, assess and remedy the data to reduce errors in the analysis.

60% of the time of a process analyst is spent in data cleaning. Considering this, a few approaches have emerged that enable automated detection and repair of errors in a log.

This project aims to gain an understanding of such approaches present in different process mining tools.

Research activities

You will work with your project supervisor in an area of potential contribution to theory and practice. This project involves the following:

  • an understanding of different approaches present in the process mining tools to improve log quality
  • assessing the approaches in data mining tools to improve log quality
  • synthesizing existing techniques and reporting on them.


We expect to create a catalogue of different approaches in the data and process mining areas that assist in improved log quality.

Skills and experience

For you to be considered for this project, a sound understanding of process or data mining would be beneficial.


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

Annual scholarship round



Contact the supervisor for more information.