IAB322 Process Mining
To view more information for this unit, select Unit Outline from the list below. Please note the teaching period for which the Unit Outline is relevant.
| Unit code: | IAB322 |
|---|---|
| Prerequisite(s): | IAB203 or DSB101 |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
Unit Outline: Semester 1 2027, Gardens Point, Internal
| Unit code: | IAB322 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | IAB203 or DSB101 |
Overview
Process mining uses data science methods to transform real-world data into process models that describe what an organisation does. This can be combined with the concepts and insights of Business Process Management to fix bottlenecks, improve delivery, ensure policies are followed, and understand change. You will learn how process mining algorithms work, and the trade-offs between different algorithms and tools. You will work hands-on with multiple process mining tools and real-world data to create your own analysis and provide data-driven recommendations on how organisations can improve. We will also discuss how process analytics connects to and complements innovations in machine learning and Artificial Intelligence.
Learning Outcomes
On successful completion of this unit you will be able to:
- Explain and analyse processes using fundamental data structures from process modelling and process mining.
- Perform an extended data-driven analysis using process mining.
- Understand machine learning and artificial intelligence concepts and their connections to processes.
- Collaborate with peers in a team environment to manage process mining projects efficiently.
- Communicate professionally in written and oral formats to a specialist audience.
Content
This unit introduces the fundamentals of process mining, including concepts, tools, and real-world challenges. Process mining concepts, data structures and algorithms lay the groundwork for understanding and explaining how these data science techniques work. You will work hands-on with industry-standard process mining tools, both in weekly exercises and extended project work. You will critically evaluate the quality of models and analyses produced by process mining tools using machine learning concepts and metrics. This unit also discusses the challenges of data-driven analysis when dealing with real-world data in industry settings, through case studies and principles of engagement with diverse stakeholders, each with their own perspectives and ways of thinking. The unit demonstrates how process mining has been utilised in an Indigenous case study.
Learning Approaches
This unit is available to study in both on-campus and online modes. The content of the unit is delivered through lectures, workshops, and the QUT Canvas site. Pre-recorded lectures cover theoretical aspects of the unit, and workshops provide an opportunity to solve practical exercises. The unit emphasises a “hands-on” approach to learning through the illustration of new concepts through worked examples and demonstrations.
You can expect to spend between 10 - 15 hours per week studying this unit, including preparing for and attending all scheduled workshops, completing assessment tasks, and undertaking your own independent study to consolidate your learning. You will also collaborate with peers to solve realistic process analytics problems.
Feedback on Learning and Assessment
The assignment and exam will be based on the material covered in the lectures and workshops. The related tasks will be discussed during these contact hours. This will provide you with a deeper understanding of the nature of the tasks. Selected supporting resources will be provided on the unit homepage. A marking guide will be available and discussed in class. Assignment marking will provide you with comprehensive feedback. Teaching staff are available during the workshops and consultation hours to clarify or elaborate on the assignment and exam content and provide constructive feedback. Example questions for the exam will be provided during the workshops and on the unit homepage.
Assessment
Overview
The assessment in this unit has been designed to help you to apply your knowledge of process mining. The assessment tasks will also allow you to demonstrate important employability skills, such as being able to work collaboratively with others and to demonstrate effective written and visual communication.
There is one group assignment (with individual components) and a final exam.
The assignment is conducted as a team, with individual components, on an extended process mining project with formative feedback throughout the semester. Tasks will be discussed during the tutorials and/or pre-recorded lectures. Selected supporting resources will be provided on the unit Canvas site. A detailed marking rubric will be available for the assessment.
The final exam will be held during the central examination period and will assess all unit content.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Process Mining Project
This group assignment demonstrates your knowledge of foundational process mining concepts and the use of related tools. Working as a team, you will produce a report based on scenarios and realistic data, and show your understanding of the mechanisms at work. Formative feedback will be given at checkpoints throughout the teaching semester. This assignment also includes individual components. The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Exam
This invigilated exam covers all the taught content for this course. The exam is invigilated, and you will be required to attend the campus or an assessment centre.The use of generative artificial intelligence (GenAI) tools is prohibited during this assessment.
Academic Integrity
Academic integrity is a commitment to undertaking academic work and assessment in a manner that is ethical, fair, honest, respectful and accountable.
The Academic Integrity Policy sets out the range of conduct that can be a failure to maintain the standards of academic integrity. This includes, cheating in exams, plagiarism, self-plagiarism, collusion and contract cheating. It also includes providing fraudulent or altered documentation in support of an academic concession application, for example an assignment extension or a deferred exam.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Breaching QUT’s Academic Integrity Policy or engaging in conduct that may defeat or compromise the purpose of assessment can lead to a finding of student misconduct (Code of Conduct – Student) and result in the imposition of penalties under the Management of Student Misconduct Policy, ranging from a grade reduction to exclusion from QUT.
Requirements to Study
Costs
No extraordinary charges or costs
Resources
All resource materials will be available in the unit Canvas site. Below are some recommended texts that you might like to access to support your learning in this unit.
Resource Materials
Recommended text(s)
Van Der Aalst, Wil: Process mining: Data science in action, Springer Berlin Heidelberg, 2016.
Risk Assessment Statement
There are no unusual health or safety risks associated with this unit
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.IN01 Bachelor of Information Technology
- Demonstrate a broad theoretical and technical knowledge of well-established and emerging IT disciplines, with in-depth knowledge in at least one specialist area aligned to multiple ICT professional roles.
Relates to: Process Mining Project, Exam - Critically analyse and conceptualise complex IT challenges and opportunities using modelling, abstraction, ideation and problem-solving to generate, evaluate and justify recommended solutions.
Relates to: Process Mining Project, Exam - Integrate and apply technical knowledge and skills to analyse, design, build, operate and maintain sustainable, secure IT systems using industry-standard tools, technologies, platforms, and processes.
Relates to: Process Mining Project, Exam - Demonstrate initiative, autonomy and personal responsibility for continuous learning, working both independently and collaboratively within multi-disciplinary teams, employing state-of-the-art IT project management methodologies to plan and manage time, resources, and risk.
Relates to: Process Mining Project - Communicate professionally and effectively in written, verbal and visual formats to a diverse range of stakeholders, considering the audience and explaining complex ideas in a simple and understandable manner in a range of IT-related contexts.
Relates to: Process Mining Project - Assess the risks and potential of artificial intelligence (and other disruptive emerging technologies) within an organisation and leverage AI knowledge and skills to solve IT challenges, improve productivity and add value.
Relates to: Exam - Critically reflect, using a human-centric approach, on the social, cultural, ethical, privacy, legal, sustainability, and accessibility issues shaping the development and use of IT, including respecting the perspectives and knowledge systems of Aboriginal and Torres Strait Islander peoples, ensuring IT solutions empower and support people with disabilities, and fostering inclusive and equitable digital technologies that serve diverse communities.
Relates to: Exam
Unit Outline: Semester 1 2027, Online
| Unit code: | IAB322 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | IAB203 or DSB101 |
Overview
Process mining uses data science methods to transform real-world data into process models that describe what an organisation does. This can be combined with the concepts and insights of Business Process Management to fix bottlenecks, improve delivery, ensure policies are followed, and understand change. You will learn how process mining algorithms work, and the trade-offs between different algorithms and tools. You will work hands-on with multiple process mining tools and real-world data to create your own analysis and provide data-driven recommendations on how organisations can improve. We will also discuss how process analytics connects to and complements innovations in machine learning and Artificial Intelligence.
Learning Outcomes
On successful completion of this unit you will be able to:
- Explain and analyse processes using fundamental data structures from process modelling and process mining.
- Perform an extended data-driven analysis using process mining.
- Understand machine learning and artificial intelligence concepts and their connections to processes.
- Collaborate with peers in a team environment to manage process mining projects efficiently.
- Communicate professionally in written and oral formats to a specialist audience.
Content
This unit introduces the fundamentals of process mining, including concepts, tools, and real-world challenges. Process mining concepts, data structures and algorithms lay the groundwork for understanding and explaining how these data science techniques work. You will work hands-on with industry-standard process mining tools, both in weekly exercises and extended project work. You will critically evaluate the quality of models and analyses produced by process mining tools using machine learning concepts and metrics. This unit also discusses the challenges of data-driven analysis when dealing with real-world data in industry settings, through case studies and principles of engagement with diverse stakeholders, each with their own perspectives and ways of thinking. The unit demonstrates how process mining has been utilised in an Indigenous case study.
Learning Approaches
This unit is available to study in both on-campus and online modes. The content of the unit is delivered through lectures, workshops, and the QUT Canvas site. Pre-recorded lectures cover theoretical aspects of the unit, and workshops provide an opportunity to solve practical exercises. The unit emphasises a “hands-on” approach to learning through the illustration of new concepts through worked examples and demonstrations.
You can expect to spend between 10 - 15 hours per week studying this unit, including preparing for and attending all scheduled workshops, completing assessment tasks, and undertaking your own independent study to consolidate your learning. You will also collaborate with peers to solve realistic process analytics problems.
Feedback on Learning and Assessment
The assignment and exam will be based on the material covered in the lectures and workshops. The related tasks will be discussed during these contact hours. This will provide you with a deeper understanding of the nature of the tasks. Selected supporting resources will be provided on the unit homepage. A marking guide will be available and discussed in class. Assignment marking will provide you with comprehensive feedback. Teaching staff are available during the workshops and consultation hours to clarify or elaborate on the assignment and exam content and provide constructive feedback. Example questions for the exam will be provided during the workshops and on the unit homepage.
Assessment
Overview
The assessment in this unit has been designed to help you to apply your knowledge of process mining. The assessment tasks will also allow you to demonstrate important employability skills, such as being able to work collaboratively with others and to demonstrate effective written and visual communication.
There is one group assignment (with individual components) and a final exam.
The assignment is conducted as a team, with individual components, on an extended process mining project with formative feedback throughout the semester. Tasks will be discussed during the tutorials and/or pre-recorded lectures. Selected supporting resources will be provided on the unit Canvas site. A detailed marking rubric will be available for the assessment.
The final exam will be held during the central examination period and will assess all unit content.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Process Mining Project
This group assignment demonstrates your knowledge of foundational process mining concepts and the use of related tools. Working as a team, you will produce a report based on scenarios and realistic data, and show your understanding of the mechanisms at work. Formative feedback will be given at checkpoints throughout the teaching semester. This assignment also includes individual components. The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Exam
This invigilated exam covers all the taught content for this course. The exam is invigilated, and you will be required to attend the campus or an assessment centre.The use of generative artificial intelligence (GenAI) tools is prohibited during this assessment.
Academic Integrity
Academic integrity is a commitment to undertaking academic work and assessment in a manner that is ethical, fair, honest, respectful and accountable.
The Academic Integrity Policy sets out the range of conduct that can be a failure to maintain the standards of academic integrity. This includes, cheating in exams, plagiarism, self-plagiarism, collusion and contract cheating. It also includes providing fraudulent or altered documentation in support of an academic concession application, for example an assignment extension or a deferred exam.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Breaching QUT’s Academic Integrity Policy or engaging in conduct that may defeat or compromise the purpose of assessment can lead to a finding of student misconduct (Code of Conduct – Student) and result in the imposition of penalties under the Management of Student Misconduct Policy, ranging from a grade reduction to exclusion from QUT.
Requirements to Study
Costs
No extraordinary charges or costs
Resources
All resource materials will be available in the unit Canvas site. Below are some recommended texts that you might like to access to support your learning in this unit.
Resource Materials
Recommended text(s)
Van Der Aalst, Wil: Process mining: Data science in action, Springer Berlin Heidelberg, 2016.
Risk Assessment Statement
There are no unusual health or safety risks associated with this unit
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.IN01 Bachelor of Information Technology
- Demonstrate a broad theoretical and technical knowledge of well-established and emerging IT disciplines, with in-depth knowledge in at least one specialist area aligned to multiple ICT professional roles.
Relates to: Process Mining Project, Exam - Critically analyse and conceptualise complex IT challenges and opportunities using modelling, abstraction, ideation and problem-solving to generate, evaluate and justify recommended solutions.
Relates to: Process Mining Project, Exam - Integrate and apply technical knowledge and skills to analyse, design, build, operate and maintain sustainable, secure IT systems using industry-standard tools, technologies, platforms, and processes.
Relates to: Process Mining Project, Exam - Demonstrate initiative, autonomy and personal responsibility for continuous learning, working both independently and collaboratively within multi-disciplinary teams, employing state-of-the-art IT project management methodologies to plan and manage time, resources, and risk.
Relates to: Process Mining Project - Communicate professionally and effectively in written, verbal and visual formats to a diverse range of stakeholders, considering the audience and explaining complex ideas in a simple and understandable manner in a range of IT-related contexts.
Relates to: Process Mining Project - Assess the risks and potential of artificial intelligence (and other disruptive emerging technologies) within an organisation and leverage AI knowledge and skills to solve IT challenges, improve productivity and add value.
Relates to: Exam - Critically reflect, using a human-centric approach, on the social, cultural, ethical, privacy, legal, sustainability, and accessibility issues shaping the development and use of IT, including respecting the perspectives and knowledge systems of Aboriginal and Torres Strait Islander peoples, ensuring IT solutions empower and support people with disabilities, and fostering inclusive and equitable digital technologies that serve diverse communities.
Relates to: Exam