Outline

Overview

This unit of study gives a comprehensive overview of post-relational data models and of latest developments in data storage technology. Particular emphasis is put on spatial, temporal, and NoSQL data storage. This unit extensively covers the advanced features of SQL:2003, as well as a few dominant NoSQL storage technologies. Besides in lectures, the advanced topics will be also studied with prescribed readings of database research publications.

Unit details and rules

Academic unit	Computer Science
Credit points	6
Prerequisites ?	(DATA3888 or COMP3888 or COMP3988 or CSEC3888 or ISYS3888 or SOFT3888 or ENGG3112 or SCPU3001) and (INFO2120 or INFO2820 or ISYS2120)
Corequisites ?	None
Prohibitions ?	COMP5338 or OCMP5338
Assumed knowledge ?	A major in a computer science area
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Ying Zhou, ying.zhou@sydney.edu.au

Assessment

The census date for this unit availability is 1 September 2025

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam ?	Final Exam Final Exam	50%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Data analysis	MongoDB project Report, MongoDB query scripts	20%	Week 07 Due date: 19 Sep 2025 at 23:59	2 weeks	AI allowed
Outcomes assessed:
Data analysis	Neo4j Project Neo4j project involving data modelling and query implementation.	20%	Week 12 Due date: 31 Oct 2025 at 23:59	2 weeks	AI allowed
Outcomes assessed:
Out-of-class quiz	Weekly quizzes Weekly quizzes	10%	Weekly	Up to 10 minutes	AI allowed
Outcomes assessed:
= hurdle task ?

Assessment summary

Out-of-class quiz: weekly Canvas quiz to be completed at the student's own time.
MongoDB project: individual assignment tests student’s ability to creat schema for a given data set and to design and implement queries on MongoDB. Deliverables include various query scripts, and a report describing the index usage and query performance.
Neo4j project: individual assignment tests student’s ability to create schema for a given problem domain and to create sample data and queries on Neo4j to demonstrate that the schema can support various queries in the problem domain. Deliverable includes executable scripts and a short description of the query design and performance observation as well as sample data file.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard. It is a policy of the School of Computer Science that in order to pass this unit, a student must achieve at least 40% in the written examination. For subjects without a final exam, the 40% minimum requirement applies to the corresponding major assessment component specified by the lecturer. A student must also achieve an overall final mark of 50 or more. Any student not meeting these requirements may be given a maximum final mark of no more than 45 regardless of their average.

For more information see sydney.edu.au/students/guide-to-grades.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	1. Introduction and organisation; 2. Motivation and NoSQL Introduction	Lecture (2 hr)
Week 01	MongoDB Installation Helpdesk	Workshop (1 hr)
Week 02	Document stores: data model and simple queries	Lecture (2 hr)
Week 02	MongoDB Basic Queries	Practical (1 hr)
Week 03	Document stores: MongoDB Aggregation Framework	Lecture (2 hr)
Week 03	MongoDB Aggregation	Practical (1 hr)
Week 04	MongoDB Join and Data Modeling	Lecture (2 hr)
Week 04	MongoDB Join and Data Modeling	Practical (1 hr)
Week 05	Document stores: MongoDB Indexing	Lecture (2 hr)
Week 05	MongoDB Indexing	Practical (1 hr)
Week 06	Document Store: MongoDB Replication and Sharding	Lecture (2 hr)
Week 06	MongoDB Replication	Workshop (1 hr)
Week 07	Spatial Model and Query	Lecture (2 hr)
Week 07	MongoDB Spatial	Practical (1 hr)
Week 08	Graph Data Model and Neo4j Introduction	Practical (2 hr)
Week 08	Neo4j Basic Queries	Practical (1 hr)
Week 09	Neo4j functions	Lecture (2 hr)
Week 09	Neo4j functions	Practical (1 hr)
Week 10	Neo4j Internal and Data Modelling	Lecture (2 hr)
Week 10	Neo4j Internal and Data Modelling	Practical (1 hr)
Week 11	Time Series Database	Lecture (2 hr)
Week 11	Time Series Database	Workshop (1 hr)
Week 12	Vector Database	Lecture (2 hr)
Week 12	Vector database	Workshop (1 hr)
Week 13	Revision	Lecture (2 hr)

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. At the completion of this unit, you should understand various NoSQL data model including document store, key-value data model, spatial model, time series data models and more
LO2. At the completion of this unit, you should be able to write simple CRUD queries and implement aggregation in MongoDB and Neo4j.
LO3. At the completion of this unit, you should understand the index mechanisms in various database systems.
LO4. At the completion of this unit, you should be able to analyse and tune the query performance for MongoDB and Neo4j.
LO5. At the completion of this unit, you should understand key issues such as partition, replication and fault tolerance in distributed database systems.
LO6. At the completion of this unit, you should understand physical storage and their impacts on query performance

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

This the first offering.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

COMP4338: Advanced Data Models

Semester 2, 2025 [Normal evening] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links