Faculty/School

Faculty of Science

School of Information Systems

Topic status

We're looking for students to study this topic.

Research centre

Centre for Data Science

Supervisors

Adjunct Professor Michael Leyer
Position
Adjunct Professor
Division / Faculty
Faculty of Business & Law
Associate Professor Chun Ouyang
Position
Associate Professor
Division / Faculty
Faculty of Science
Dr Roy Yang
Position
Postdoctoral Research Fellow
Division / Faculty
Faculty of Science

Overview

odern information systems in today’s organisations record massive amount of event log data capturing the execution of day-to-day core processes within and across organisations. Mining these event log data to drive process analytics and knowledge discovery is known as process mining. To date various process mining techniques have been developed to help extract insights about the actual processes with the ultimate goal to organisations' workforce capability and capacity building.

As an important sub-field of process mining, organisational mining focuses on discovering organisational knowledge, including e.g. organisational structures and human resources relevant to the performance of an organisation's core processes, and continuously evolving organisational dynamics. In any organisation where humans play a dominant role, process-aware workforce analytics helps managers gain a better understanding of the de facto grouping of human resources (e.g. team formation and dynamics) and their interactions thus to improve the related processes as well as the organisational performance towards the building of a healthy and sustainable workforce.

Research engagement

Students will engage with literature review, design of new approach or technique, and evaluation via experiments or user studies.

Research activities

The research activities below can be scoped to cater for different types of research student projects.
  • Literature review on state-of-the-art approaches and techniques related to organisational mining.
  • Design of new algorithms and techniques for workforce analytics based on study and application of various data mining techniques.
  • Design of new algorithms and techniques for workforce analytics based on study and application of existing social network analysis techniques.
  • Design of a systematic approach for actionable process improvement informed by the findings from organisational mining.
  • Implementation of the new algorithms, techniques and approaches, visualisation of the results and finding, and evaluation.

Outcomes

  • New/improved methods and algorithms for organisational model mining built upon suitable data mining techniques.
  • Novel approach and models for discovering, reasoning, and analysing (intra-)organisational and inter-organisational networks by leveraging existing social network analysis capabilities.
  • New tools and visualisation of the discovered organisational networks.
  • Knowledge discovery from process event logs for organisational improvement informed by management theories and principles.

Skills and experience

  • Familiarity with the fields of data mining (and preferably process mining and/or social network analysis).
  • Problem-solving and critical thinking capabilities.
  • Programming skills in Python.
  • Academic writing skills.

Start date

18 November, 2024

End date

14 February, 2025

Location

Gardens Point Campus

Keywords

Contact

Chun Ouyang <c.ouyang@qut.edu.au>