Study level

PhD

Master of Philosophy

Honours

Vacation research experience scheme

Faculty/School

Science and Engineering Faculty

School of Information Systems

Topic status

We're looking for students to study this topic.

Supervisors

Dr Kanika Goel
Position
Associate Lecturer
Division / Faculty
Science and Engineering Faculty
Dr Sander Leemans
Position
Lecturer in Business Process Management
Division / Faculty
Science and Engineering Faculty

Overview

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 errors in the analysis. 60% of a process analyst's time 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'll work with your project supervisor in an area of potential contribution to theory and practice.

This project involves:

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

Outcomes

Upon conclusion of this research project, we expect to create a catalogue of different approaches in the data and process mining areas that assist in improving log quality.

Skills and experience

A sound understanding of process or data mining would be beneficial.

Keywords

Contact

Contact the supervisor for more information.