Preventing data quality problems in process mining

Overview

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 prevention of data quality problems in the first place.

Research activities

You'll be working on methodological and technical aspects of the prevention of data quality problems in process mining. It's expected that this will include case studies with industry.

Your supervisor, Professor Arthur ter Hofstede, is an internationally-recognized business process management (BPM) researcher. He's known for his work on workflow patterns and YAWL. According to Guide2Research, he's Queensland's highest-ranked computer scientist, in terms of h-index score.

Our BPM team is a well-respected, connected and well-known research group with a long history and a breadth of research covering both information systems and computer science aspects.

Outcomes

Outcomes will include a detailed approach towards the prevention of data quality problems in process mining based on empirical insights and a theoretical framework.

Skills and experience

A background in process or data mining would be helpful as well as data analysis skills.

We expect you to have skills in implementation.

A willingness to delve into aspects of information systems methodology is appreciated.

Scholarships

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

Annual scholarship round

Keywords

• process mining
• data quality

Contact

Contact the supervisor for more information.