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Unit outline_

OCMP5338: Advanced Data Models

Semester 2a, 2023 [Online] - Online Program

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
Type Description Weight Due Length
Tutorial quiz Knowledge Check Week 1
Knowledge Check
0% Week 01 30 mins
Outcomes assessed: LO1
Assignment Weekly Assignment 1
MongoDB basic query
10% Week 01 300 mins
Outcomes assessed: LO2
Tutorial quiz Knowledge Check Week 2
Knowledge Check
0% Week 02 30 mins
Outcomes assessed: LO1 LO2
Assignment Weekly Assignment 2
MongoDB aggregation
10% Week 02 300 mins
Outcomes assessed: LO1 LO2 LO3 LO4
Tutorial quiz Knowledge Check Week 3
Knowledge Check
0% Week 03 30 mins
Outcomes assessed: LO3 LO5 LO4
Assignment Weekly Assignment 3
MongoDB performance
10% Week 03 300 mins
Outcomes assessed: LO1 LO2 LO3 LO4
Tutorial quiz Knowledge Check Week 4
Knowledge Check
0% Week 04 30 mins
Outcomes assessed: LO1 LO6 LO5 LO3 LO2
Assignment Weekly Assignment 4
Modelling exercise
10% Week 04 300 mins
Outcomes assessed: LO1
Tutorial quiz Knowledge Check Week 5
Knowledge Check
0% Week 05 30 mins
Outcomes assessed: LO1 LO2
Assignment Weekly Assignment 5
Neo4j basic query
10% Week 05 300 mins
Outcomes assessed: LO2
Tutorial quiz Knowledge Check Week 6
Knowledge Check
0% Week 06 30 mins
Outcomes assessed: LO1 LO6 LO4 LO3 LO2
Assignment Weekly Assignment 6
Neo4j modeling, query and aggregation
10% Week 06 600 mins
Outcomes assessed: LO1 LO2 LO3 LO4
Supervised exam
? 
Final Exam
Final Exam taken in Week 8
40% Week 08
Due date: 22 Sep 2023 at 18:30
2 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6

Assessment summary

Assignment 1: MongoDB Basic Query

Assignment 2: MongoDB Aggregation

Assignment 3: MongoDB Performance

Assignment 4: Modelling Exercise

Assignment 5: Neo4j Basic Query

Assignment 6: Neo4j Modelling, query and aggregation

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.

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 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.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

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.

WK Topic Learning activity Learning outcomes
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) LO1 LO2
*Welcome and Knowledge Check *Q&A *Workshop *Preview Weekly Assignment Workshop (1.5 hr) LO1 LO2
Week 02 *Getting Started *Null Type *Mongo DB Aggregation Framework *Write Operation Feature *MongoDB Data Modelling *Workshop Instruction Independent study (2.25 hr) LO1 LO2
*Welcome and Knowledge Check *Q&A *Workshop *Preview Weekly Assignment Workshop (1.5 hr) LO1 LO2
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) LO3 LO4 LO5
*Welcome and Knowledge Check *Q&A *Workshop *Preview Weekly Assignment Workshop (1.5 hr) LO3 LO4 LO5
Week 04 *Getting Started *Spatial Model and Query *Spatial Model Index Mechanisms *Data with multiple versions *Time series Database *Workshop Instruction Independent study (2.75 hr) LO1 LO2 LO3 LO5 LO6
*Welcome and Knowledge Check *Q&A *Workshop *Preview Weekly Assignment Workshop (1.5 hr) LO1 LO2 LO3 LO5 LO6
Week 05 *Getting Started *Property Graph Model *Basic Cypther Query *Neo4j Functions *Workshop Instruction Independent study (2.5 hr) LO1 LO2
*Welcome and Knowledge Check *Q&A *Workshop *Preview Weekly Assignment Workshop (1.5 hr) LO1 LO2
Week 06 *Getting Started *Cypher - MERGE Clause *Other Options of Storing Graph *Neo4j Storage *Neo4j Query Execution *Workshop Instruction Independent study (2.66 hr) LO1 LO2 LO3 LO4 LO6
*Welcome and Knowledge Check *Q&A *Workshop *Preview Weekly Assignment Workshop (1.5 hr) LO1 LO2 LO3 LO4 LO6

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. 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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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

This is a new unit.

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.