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Unit of study_

COMP5338: Advanced Data Models

Semester 2, 2022 [Normal evening] - Remote

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

Unit code COMP5338
Academic unit Computer Science
Credit points 6
Prohibitions
? 
None
Prerequisites
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None
Corequisites
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None
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

Yes

Teaching staff

Coordinator Ying Zhou, ying.zhou@sydney.edu.au
Type Description Weight Due Length
Final exam (Take-home short release) Type D final exam Final Exam
Online open book without invigilation
40% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
Assignment MongoDB Basic Queries
MongoDB query scripts
10% Week 03
Due date: 18 Aug 2022 at 23:59
Max 5 queries
Outcomes assessed: LO2
Assignment MongoDB project
Report, MongoDB query scripts
20% Week 07
Due date: 15 Sep 2022 at 23:59
Max 10 pages, max 10 queries
Outcomes assessed: LO1 LO2 LO3 LO4
Assignment Neo4j Basic Queries
cypher query scripts
10% Week 09
Due date: 06 Oct 2022 at 23:59
Max 5 queries
Outcomes assessed: LO2
Assignment Neo4j Project
Neo4j Browser Guide
20% Week 13
Due date: 03 Nov 2022 at 23:59
Max 10 pages
Outcomes assessed: LO1 LO2 LO3 LO4
Type D final exam = Type D final exam ?

Assessment summary

  • MongoDB basic queries: individual assignment tests student’s ability to writing simple MongoDB queries with a given schema  and sample data. Deliverables include various query scripts.
  • 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 basic queries: invidual assignment tests student’s ability write basic query with a given schema and sample data. Deliverables include various query scripts.
  • 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 a browser guide with executable script and 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.

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.

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.

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 1. Introduction and organisation; 2. Motivation and NoSQL Introduction Lecture (2 hr) LO3
MongoDB Installation Helpdesk Workshop (1 hr)  
Week 02 Document stores: data model and simple queries Lecture (2 hr) LO2
MongoDB Basic Queries Practical (1 hr) LO1 LO2
Week 03 Document stores: MongoDB Aggregation Framework Lecture (2 hr) LO1 LO2 LO4
MongoDB Aggregation Practical (1 hr) LO2 LO4
Week 04 Document stores: MongoDB Indexing Lecture (2 hr) LO4 LO5
MongoDB Indexing Practical (1 hr) LO1 LO4
Week 05 Document Store: MongoDB Replication and Sharding Lecture (2 hr) LO5
MongoDB Replicaiton Workshop (1 hr) LO5
Week 06 Spatial Model and Query Lecture (2 hr) LO1 LO2
MongoDB Spatial Practical (1 hr) LO1 LO2
Week 07 Spatial Index Lecture (2 hr) LO1 LO4 LO6
Spatial Index Workshop (1 hr) LO1 LO4
Week 08 Graph Data Model and Neo4j Introduction Lecture (2 hr) LO2
Neo4j Basic Queries Practical (1 hr) LO1 LO2
Week 09 Neo4j functions Lecture (2 hr) LO2
Neo4j functions Practical (1 hr) LO2
Neo4j Internal and Data Modeling Practical (1 hr) LO1 LO4 LO6
Week 10 Neo4j Internal and Data Modelling Lecture (2 hr) LO1 LO4 LO6
Week 11 Data with multiple versions Lecture (2 hr) LO1 LO5
Google Bigtable Workshop (1 hr) LO5
Week 12 Time Series Database Lecture (2 hr) LO1 LO5
Time Series Database Workshop (1 hr) LO1 LO5
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 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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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

Add to small assignments to enable early feedback on basic data model and queries.

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.

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.