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Unit of study_

COMP5338: Advanced Data Models

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.


Academic unit Computer Science
Unit code COMP5338
Unit name Advanced Data Models
Session, year
Semester 2, 2021
Attendance mode Normal evening
Location Remote
Credit points 6

Enrolment rules

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


Teaching staff and contact details

Coordinator Ying Zhou,
Type Description Weight Due Length
Final exam (Take-home short release) Type D final exam Final Exam
Online open book without invigilation
60% Formal exam period 2 hours
Outcomes assessed: LO1 LO5 LO4 LO3 LO2
Assignment MongoDB Assignment
Practical MongoDB Assignment
20% Week 07 Max 10 page report + 5 min demo
Outcomes assessed: LO1 LO2 LO4 LO6
Assignment Neo4j Assignment
Practical Neo4j Assignment
20% Week 13 Max 10 page report + 5 min demo
Outcomes assessed: LO1 LO2 LO4 LO6
Type D final exam = Type D final exam ?
  • MongoDB assignment: individual assignment tests student’s ability to creat schema for a given data set and to design and implement queries on MongoDB. Deliverable includes a short report and a demo.
  • Neo4j assiglnment: individual assignment tests student’s ability to create schema for a given data set and to design and implement queries on Neo4j. Deliverable includes a short report and a demo

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


High distinction

85 - 100



75 - 84



65 - 74



50 - 64



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

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.

Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

WK Topic Learning activity Learning outcomes
Week 01 1. Introduction and organisation; 2. Motivation and NoSQL Introduction Lecture (2 hr) LO5
MongoDB Installation Helpdesk Workshop (1 hr)  
Week 02 Document stores: data model and simple queries Lecture (2 hr) LO4
MongoDB Basic Queries Practical (1 hr) LO2 LO4
Week 03 Document stores: MongoDB Aggregation Framework Lecture (2 hr) LO1 LO2 LO4
MongoDB Aggregation Practical (1 hr) LO1 LO4
Week 04 Document stores: MongoDB Indexing Lecture (2 hr) LO1 LO3
MongoDB Indexing Practical (1 hr) LO1 LO2
Week 05 Document Store: MongoDB Replication and Sharding Lecture (2 hr) LO3
MongoDB Replicaiton Workshop (1 hr) LO3
Week 06 Spatial Model and Query Lecture (2 hr) LO2 LO4
MongoDB Spatial Practical (1 hr) LO2 LO4
Week 07 Spatial Index Lecture (2 hr) LO1 LO2 LO6
Spatial Index Workshop (1 hr) LO1 LO2
Week 08 Graph Data Model and Neo4j Introduction Lecture (2 hr) LO4
Neo4j Basic Queries Practical (1 hr) LO2 LO4
Week 09 Neo4j Internal and Data Modelling Lecture (2 hr) LO1
Neo4j Internal and Data Modeling Practical (1 hr) LO1 LO2 LO4
Week 10 Key-value storage Lecture (2 hr) LO2 LO3
Amazon Dynamo Workshop (1 hr) LO2 LO3
Week 11 Log Structured Merge Tree and Google Bigtable Lecture (2 hr) LO2 LO3
Google Bigtable Workshop (1 hr) LO3
Week 12 Time Series Database Lecture (2 hr) LO1 LO2 LO3
Time Series Database Practical (1 hr) LO4
Week 13 Revision Lecture (2 hr) LO1 LO2 LO3 LO4 LO5 LO6

Attendance and class requirements

Study commitment: Students are expected to attend all scheduled lectures, and laboratory classes. It should be realised that laboratory exercises are expected to take longer than just the time scheduled for classes. Students are expected to self-dependently prepare the prescribed research paper readings and conduct additional literature and system research as necessary. Students are expected to be able to work independently and to make effective use of a range of resources including the library, the Internet and relevant on-line help facilities. Students are expected to check their progressive results regularly. Results will be published through USYD eLearning. Any errors or omissions must be reported to the unit coordinator, with appropriate evidence, as soon as possible. Please note: Marks are considered to have been confirmed ten days after being published and will not subsequently be altered.

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 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.

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

  • LO1. At the completion of this unit, you should be able to analyse and tune the query performance on a range of databaae systems
  • LO2. At the completion of this unit, you should be able to understand various NoSQL data model including document store, key-value data model, spatial model, time series data models and more
  • LO3. At the completion of this unit, you should be able to describe the basic concepts of advanced data management topics such as big data storage and processing, as well as distributed data management architecture
  • LO4. At the completion of this unit, you should be able to mange and write simple or aggregate queries on a range of database systems including MongoDB, Neo4j and others.
  • LO5. At the completion of this unit, you should be able to describe the difference between polyglot persistence and the multi model database systems.
  • LO6. At the completion of this unit, you should be able to collect information, develop evidence and and present your findings on practical database topic

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
Removed group projects which did not go well in online setting. All assessments are individual submitted work.

IMPORTANT: School policy relating to Academic Dishonesty and Plagiarism.

In assessing a piece of submitted work, the School of Computer Science may reproduce it entirely, may provide a copy to another member of faculty, and/or to an external plagiarism checking service or in-house computer program and may also maintain a copy of the assignment for future checking purposes and/or allow an external service to do so. 

All written assignments submitted in this unit of study will be submitted to the similarity detecting software program known as Turnitin. Turnitin searches for matches between text in your written assessment task and text sourced from the Internet, published works and assignments that have previously been submitted to Turnitin for analysis.

There will always be some degree of text-matching when using Turnitin. Text-matching may occur in use of direct quotations, technical terms and phrases, or the listing of bibliographic material. This does not mean you will automatically be accused of academic dishonesty or plagiarism, although Turnitin reports may be used as evidence in academic dishonesty and plagiarism decision-making processes.


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

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