Course coordinator
Dr Yadan Luo
Course overview
- Study period
- Semester 1, 2025 (24/02/2025 - 21/06/2025)
- Study level
- Undergraduate
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Elec Engineering & Comp Science School
Selected advanced topics from spatial and multimedia databases: multidimensional data management concepts, theories and technologies, focusing on data access methods and similarity query processing for spatial, multimedia and Web-based databases, with particular emphasis on video indexing and search.
This course explores advanced database topics designed to cultivate critical thinking about techniques for managing large-scale, complex data. Many application domains—such as spatial, multimedia, and scientific fields—often represent data in multidimensional formats. Students will be introduced to the key concepts, theories, and technologies underlying multidimensional data management, with a particular emphasis on data access methods and similarity query processing for spatial and multimedia datasets. Additionally, the course will cover specialized topics, including the application of AI in trajectory analysis.
In response to feedback from our previous offering, we have redesigned the project assessment to provide greater flexibility in development options. For example, we will demonstrate an AI-assisted full-stack application that uses PostgreSQL as its database, giving students the opportunity to build and deploy a real spatio-temporal web application.
Course requirements
Assumed background
Students are assumed to have knowledge covered in courses INFS2200/7903 Relational Database Systems and COMP3506 Algorithm and Data Structures. The following gives the minimum knowledge that students should have in order to take this course (you may refer to INFS2200 Course Profile): good knowledge of SQL, database indexing techniques, DBMS architectures, query processing and optimisation, algorithms and programming.ﾠ
Prerequisites
You'll need to complete the following courses before enrolling in this one:
(INFS2200 or INFS7903) and (COMP3506 or COMP7505)
Incompatible
You can't enrol in this course if you've already completed the following:
INFS4200 or INFS7200 or INFS7205
Jointly taught details
This course is jointly-taught with:
- INFS7205
Course contact
Course staff
Lecturer
Dr Yadan Luo
Ms Zixin Wang
Timetable
The timetable for this course is available on the UQ Public Timetable.
Aims and outcomes
This course is designed to help students to learn the latest knowledge, gain insightful understanding and develop skills of critical thinking on large-scale complex data management, for effectively and efficiently managing and searching the information of user interest beyond the traditional relational databases. Through in-depth discussions on spatiotemporal and multimedia data, in this course, we will study the theory and techniques of representing complex data objects as multidimensional feature vectors and processing similarity-based queries. These theories and techniques form the foundation of modern data management, which can also find applications in a wide range of areas such as information retrieval, Web search, bioinformatics, data mining and data science.ﾠ
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Identify the applications and features of complex data types, including spatial, spatialtemporal and multimedia data.
LO2.
Understand existing advanced spatial and multimedia processing techniques from database management and machine learning perspectives.
LO3.
Examine the major issues of spatial and multimedia data management systems.
LO4.
Analyze and evaluate existing research methods, creative to identify and define problems, and innovative for their solutions for multimedia database management.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Computer Code, Practical/ Demonstration | Prac 1 - Implementation | 5% |
28/03/2025 3:00 pm |
| Examination |
In-Semester Exam
|
15% |
14/04/2025
This exam will be held in the Lecture timeslot. However, students can access the exam online and are not required to be on campus. |
| Computer Code, Paper/ Report/ Annotation, Practical/ Demonstration, Project | Prac 2 - Project | 25% |
16/05/2025 3:00 pm |
| Examination |
Final Exam
|
55% |
End of Semester Exam Period 7/06/2025 - 21/06/2025 |
A hurdle is an assessment requirement that must be satisfied in order to receive a specific grade for the course. Check the assessment details for more information about hurdle requirements.
Assessment details
Prac 1 - Implementation
- Mode
- Activity/ Performance
- Category
- Computer Code, Practical/ Demonstration
- Weight
- 5%
- Due date
28/03/2025 3:00 pm
- Learning outcomes
- L01, L02, L03, L04
Task description
The practical practice consists of one coding task. There will be demonstrations illustrating how to perform indexing and querying in a spatio-temporal database using PostgreSQL. These examples will serve as a guide for students to accomplish the project. Additionally, there will be a specific question that students need to address using SQL scripts. These SQL scripts should be submitted through the Blackboard platform.
AI Policy. This task has been designed to be challenging, authentic, and complex. While students may use AI and/or MT technologies, successful completion will require critical engagement in specific contexts and tasks, where AI will provide only limited support and guidance. To pass this assessment, students must demonstrate detailed comprehension of their written submission independent of AI and MT tools. Failure to reference AI or MT use may result in student misconduct under the Student Code of Conduct.
Submission guidelines
Code will be submitted on Blackboard
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
This course uses a progressive assessment approach where feedback and/or detailed solutions will be released to students within 14 days.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
In-Semester Exam
- Online
- Mode
- Written
- Category
- Examination
- Weight
- 15%
- Due date
14/04/2025
This exam will be held in the Lecture timeslot. However, students can access the exam online and are not required to be on campus.
- Learning outcomes
- L01, L02, L03, L04
Task description
It is an Online Quiz (with specified materials permitted) for the knowledge covered by the lectures from week 2 to week7. This exam is held at Lecture time. However, students can access the exam online and are not required to be on campus.
AI Policy. This assessment task evaluates students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI) or Machine Translation (MT). Students are advised that the use of AI or MT technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Exam details
| Planning time | 10 minutes |
|---|---|
| Duration | 90 minutes |
| Calculator options | Any calculator permitted |
| Open/closed book | Open Book examination |
| Exam platform | Other |
| Invigilation | Not invigilated |
Submission guidelines
In-Semester exam will be submitted on Blackboard
Deferral or extension
You may be able to defer this exam.
Prac 2 - Project
- Mode
- Activity/ Performance, Written
- Category
- Computer Code, Paper/ Report/ Annotation, Practical/ Demonstration, Project
- Weight
- 25%
- Due date
16/05/2025 3:00 pm
- Learning outcomes
- L01, L02, L03, L04
Task description
Students must execute the prescribed algorithms to address the challenges outlined in the project specifications, which will be made available in Week 6. This project is intended to be completed individually, and each student is expected to provide a 4-page report detailing their implementation efforts. It is crucial for students to independently handle both the design and implementation aspects of the project. While there are no restrictions on the choice of programming languages, Python/SQL will be an integral part of this project.
AI Policy. This task has been designed to be challenging, authentic, and complex. While students may use AI and/or MT technologies, successful completion will require critical engagement in specific contexts and tasks, where AI will provide only limited support and guidance. To pass this assessment, students must demonstrate detailed comprehension of their written submission independent of AI and MT tools. Failure to reference AI or MT use may result in student misconduct under the Student Code of Conduct.
Submission guidelines
Projects will be submitted via Turnitin on Blackboard
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
This course uses a progressive assessment approach where feedback and/or detailed solutions will be released to students within 14 days.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Final Exam
- Hurdle
- Identity Verified
- In-person
- Mode
- Written
- Category
- Examination
- Weight
- 55%
- Due date
End of Semester Exam Period
7/06/2025 - 21/06/2025
- Learning outcomes
- L01, L02, L03, L04
Task description
A centrally controlled on-campus exam will be held. Timing: The final exam will be scheduled at a fixed time for all students – i.e. students will complete the exam simultaneously.
AI Policy. This assessment task is to be completed in-person. The use of generative Artificial Intelligence (AI) or Machine Translation (MT) tools will not be permitted. Any attempted use of AI or MT may constitute student misconduct under the Student Code of Conduct.
Hurdle requirements
You must score at least 50%(27.5 out of 55 marks) in the final exam to pass the course.Exam details
| Planning time | 10 minutes |
|---|---|
| Duration | 120 minutes |
| Calculator options | Any calculator permitted |
| Open/closed book | Closed Book examination - no written materials permitted |
| Exam platform | Paper based |
| Invigilation | Invigilated in person |
Submission guidelines
Deferral or extension
You may be able to defer this exam.
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Cut off Percent | Description |
|---|---|---|
| 1 (Low Fail) | 0 - 19 |
Absence of evidence of achievement of course learning outcomes. |
| 2 (Fail) | 20 - 46 |
Minimal evidence of achievement of course learning outcomes. |
| 3 (Marginal Fail) | 47 - 49 |
Demonstrated evidence of developing achievement of course learning outcomes |
| 4 (Pass) | 50 - 64 |
Demonstrated evidence of functional achievement of course learning outcomes. |
| 5 (Credit) | 65 - 74 |
Demonstrated evidence of proficient achievement of course learning outcomes. |
| 6 (Distinction) | 75 - 84 |
Demonstrated evidence of advanced achievement of course learning outcomes. |
| 7 (High Distinction) | 85 - 100 |
Demonstrated evidence of exceptional achievement of course learning outcomes. |
Additional course grading information
Note that you have to score at least 50 marks in total (out of 100 marks) and at least 50%ﾠ(27.5 out of 55 marks) for the final exam to pass the course. Percentages will not be rounded before any gradeﾠcut-offs apply.ﾠThe course coordinator reserves the right to moderate marks.
Supplementary assessment
Supplementary assessment is available for this course.
Additional assessment information
As this course is more research-oriented, we provide tutorials to further discuss the details. Students also need to focus on paper/online reading to gain the most advanced knowledge.ﾠ
Having Troubles?
If you are having difficulties with any aspect of the course material, you should seek help and speak to the course teaching staff. If external circumstances are affecting your ability to work on the course, you should seek help as soon as possible. The University and UQ Union have organisations and staff who are able to help; for example, UQ Student Services are able to help with study and exam skills, tertiary learning skills, writing skills, financial assistance, personal issues, and disability services (among other things). Complaints and criticisms should be directed in the first instance to the course coordinator. If you are not satisfied with the outcome, you may bring the matter to the attention of the School of EECS Director of Teaching and Learning.
Learning resources
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Library resources
Find the required and recommended resources for this course on the UQ Library website.
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Multiple weeks |
Lecture |
Weekly Lectures Readings/Ref: Lecture Notes; Other Learning outcomes: L01, L02, L03, L04 |
Tutorial |
Contacts Contacts will be only held between Week 2 - Week 13 Learning outcomes: L01, L02, L03, L04 |
Policies and procedures
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments - Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.
School guidelines
Your school has additional guidelines you'll need to follow for this course: