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 ?	None
Corequisites ?	None
Prohibitions ?	COMP5338 or COMP4338
Assumed knowledge ?	This unit of study assumes foundational knowledge of relational database systems as taught in COMP5138/COMP9120 (Database Management Systems) or INFO2120/INFO2820/ISYS2120 (Database Systems 1)
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Nataliia Stratiienko, nataliia.stratiienko@sydney.edu.au

Assessment

The census date for this unit availability is 22 August 2025

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Out-of-class quiz	Knowledge Check Week 1 Knowledge Check	0%	Week 01	30 mins	AI allowed
Outcomes assessed:
Out-of-class quiz	Knowledge Check Week 2 Knowledge Check	0%	Week 02	30 mins	AI allowed
Outcomes assessed:
Data analysis	Assignment 1 MongoDB Basic Queries	5%	Week 02 Due date: 12 Aug 2025 at 23:59	1 week	AI allowed
Outcomes assessed:
Out-of-class quiz	Knowledge Check Week 3 Knowledge Check	0%	Week 03	30 mins	AI allowed
Outcomes assessed:
Out-of-class quiz	Knowledge Check Week 4 Knowledge Check	0%	Week 04	30 mins	AI allowed
Outcomes assessed:
Data analysis	Assignment 2 (Part 1) MongoDB mini-project coding	12%	Week 04 Due date: 26 Aug 2025 at 23:59	2 weeks	AI allowed
Outcomes assessed:
Presentation	Assignment 2 (Part 2) MongoDB mini-project presentation	8%	Week 04 Due date: 29 Aug 2025 at 18:30	1 week	AI allowed
Outcomes assessed:
Out-of-class quiz	Knowledge Check Week 5 Knowledge Check	0%	Week 05	30 mins	AI allowed
Outcomes assessed:
Out-of-class quiz	Knowledge Check Week 6 Knowledge Check	0%	Week 06	30 mins	AI allowed
Outcomes assessed:
Data analysis	Assignment 3 (Part 1) NoSQL models project coding and report	17%	Week 06 Due date: 09 Sep 2025 at 23:59	2 weeks	AI allowed
Outcomes assessed:
Presentation	Assignment 3 (Part 2) NoSQL models project presentation	8%	Week 06 Due date: 12 Sep 2025 at 18:30	1 week	AI allowed
Outcomes assessed:
Written exam ?	Final Exam Final Exam taken in Week 8 (Proctored exam in Canvas)	50%	Week 08 Due date: 26 Sep 2025 at 18:30	2 hours	AI prohibited
Outcomes assessed:

Assessment summary

Assignment 1: MongoDB Basic Queries

Assignment 2 (Part 1): MongoDB mini-project coding

Assignment 2 (Part 2): MongoDB mini-project presentation

Assignment 3 (Part 1): NoSQL models project coding and report

Assignment 3 (Part 2): NoSQL models project presentation

Assessment criteria

Result Name

Mark Range

Description

High distinction

85 - 100

Awarded when you demonstrate the learning outcomes for the unit at an exceptional standard, as defined by grade descriptors or exemplars outlined by your faculty or school.

Distinction

75 - 84

Awarded when you demonstrate the learning outcomes for the unit at a very high standard, as defined by grade descriptors or exemplars outlined by your faculty or school

Credit

65 - 74

Awarded when you demonstrate the learning outcomes for the unit at a good standard, as defined by grade descriptors or exemplars outlined by your faculty or school.

Pass

50 - 64

Awarded when you demonstrate the learning outcomes for the unit at an acceptable standard, as defined by grade descriptors or exemplars outlined by your faculty or school.

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 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	Getting Started Data Models and Scalability Document Models Mongo DB Basic Data Model Mongo DB CRUD Queries Workshop Instruction	Independent study (2.5 hr)
Week 01	Welcome and Knowledge Check Q&A Workshop Assignment 1 Progress Check	Workshop (1.5 hr)
Week 02	Getting Started Null Type Mongo DB Aggregation Framework Write Operation Feature MongoDB Data Modelling Workshop Instruction	Independent study (2.25 hr)
Week 02	Welcome and Knowledge Check Q&A Workshop Assignment 2 (Part 1) Progress Check	Workshop (1.5 hr)
Week 03	Getting Started Mongo DB: Indexes Mongo DB: Execution Plan MongoDB: Performance Tuning MongoDB: Replication MongoDB: Sharding *Workshop Instruction	Independent study (3.16 hr)
Week 03	Welcome and Knowledge Check Q&A Workshop Assignment 2 (Part 1) Progress Check *Overview of Assignment 2 (Part 2)	Workshop (1.5 hr)
Week 04	Getting Started Property Graph Model Basic Cypther Query Neo4j Functions *Workshop Instruction	Independent study (2.5 hr)
Week 04	Welcome and Knowledge Check Q&A Workshop Assignment 2 (Part 2): Online Presentations *Assignment 3 (Part 1) Progress Check	Workshop (1.5 hr)
Week 05	Getting Started Cypher - MERGE Clause Other Options of Storing Graph Neo4j Storage Neo4j Query Execution Workshop Instruction	Independent study (2.66 hr)
Week 05	Welcome and Knowledge Check Q&A Workshop Assignment 3 (Part 1) Progress Check *Overview of Assignment 3 (Part 2)	Workshop (1.5 hr)
Week 06	Getting Started Spatial Model and Query Spatial Model Index Mechanisms Data with multiple versions Time series Database Workshop Instruction	Independent study (2.75 hr)
Week 06	Welcome and Knowledge Check Q&A Workshop Assignment 3 (Part 2): Online Presentations *Final Exam Preparation	Workshop (1.5 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. Understand various NoSQL data models including document model, graph model, key-value data model, spatial model and temporal data models
LO2. Write simple CRUD queries and implement aggregation in MongoDB and Neo4j
LO3. Understand the Index mechanisms in various database systems
LO4. Analyse and tune the query performance in MongoDB and Neo4j
LO5. Understand key issues such as partition, replication and fault tolerance in distributed database systems.
LO6. 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.

No changes have been made since this unit was last offered

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

OCMP5338: Advanced Data Models

PG Online Session 2A, 2025 [Online] - Online Program

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