COMP5338: Semester 2, 2021

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Units COMP5338 Semester 2 2021 [Normal evening]

Unit of study_

COMP5338: Advanced Data Models

Semester 2, 2021 [Normal evening] - Remote

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

Unit code	COMP5338
Academic unit	Computer Science
Credit points	6
Prohibitions ?	None
Prerequisites ?	None
Corequisites ?	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	No

Teaching staff

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

Type	Description	Weight	Due	Length
Final exam (Take-home short release)	Final Exam Online open book without invigilation	60%	Formal exam period	2 hours
Outcomes assessed:
Assignment	MongoDB Assignment Practical MongoDB Assignment	20%	Week 07	Max 10 page report + 5 min demo
Outcomes assessed:
Assignment	Neo4j Assignment Practical Neo4j Assignment	20%	Week 13	Max 10 page report + 5 min demo
Outcomes assessed:
= Type D final exam ?

Assessment summary

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	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.

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.

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	Document stores: MongoDB Indexing	Lecture (2 hr)
Week 04	MongoDB Indexing	Practical (1 hr)
Week 05	Document Store: MongoDB Replication and Sharding	Lecture (2 hr)
Week 05	MongoDB Replicaiton	Workshop (1 hr)
Week 06	Spatial Model and Query	Lecture (2 hr)
Week 06	MongoDB Spatial	Practical (1 hr)
Week 07	Spatial Index	Lecture (2 hr)
Week 07	Spatial Index	Workshop (1 hr)
Week 08	Graph Data Model and Neo4j Introduction	Lecture (2 hr)
Week 08	Neo4j Basic Queries	Practical (1 hr)
Week 09	Neo4j Internal and Data Modelling	Lecture (2 hr)
Week 09	Neo4j Internal and Data Modeling	Practical (1 hr)
Week 10	Key-value storage	Lecture (2 hr)
Week 10	Amazon Dynamo	Workshop (1 hr)
Week 11	Log Structured Merge Tree and Google Bigtable	Lecture (2 hr)
Week 11	Google Bigtable	Workshop (1 hr)
Week 12	Time Series Database	Lecture (2 hr)
Week 12	Time Series Database	Practical (1 hr)
Week 13	Revision	Lecture (2 hr)

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

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 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
Learning outcomes

Responding to student feedback

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

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

Additional information

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.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

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