COMP5338: Semester 2, 2020

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Units COMP5338 Semester 2 2020 [Normal evening]

Unit of study_

COMP5338: Advanced Data Models

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.

Details

Academic unit	Computer Science
Unit code	COMP5338
Unit name	Advanced Data Models
Session, year ?	Semester 2, 2020
Attendance mode	Normal evening
Location	Camperdown/Darlington, Sydney
Credit points	6

Enrolment rules

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 and contact details

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

Type	Description	Weight	Due	Length
Final exam (Open book)	Final Exam Online open book without invigilation	60%	Formal exam period	2 hours
Outcomes assessed:
Assignment	MongoDB Assignment Practical Assignment	10%	Week 05	Max 5 page report + 5 min demo
Outcomes assessed:
Assignment	Neo4j Assignment Practical Assignment	10%	Week 08	Max 5 page report + 5 min demo
Outcomes assessed:
Assignment	Research Project Research project (lit review, analysis and recommendation)	20%	Week 12	Max 10 page report + 10 min presentation
Outcomes assessed:
= group assignment ? = Type C 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
Research project: group work assess student’s ability to search for information related with a topic, to make argument and to produce evidence to support such argument. Deliverable include a report and a presentation.

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.

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 honesty, academic dishonesty, 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 dishonesty or plagiarism seriously.

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

Weekly schedule

WK	Topic	Learning activity
Week 01	1. Introduction and organisation; 2. Motivation and NoSQL Introduction	Lecture and tutorial (2 hr)
Week 02	Document stores: data model and queries	Lecture and tutorial (3 hr)
Week 03	Document stores: internal and performance tuning	Lecture and tutorial (3 hr)
Week 04	Document stores: Indexing and Performance Tuning	Lecture and tutorial (3 hr)
Week 05	Column based storage: data model and architecture	Lecture and tutorial (3 hr)
Week 06	Graph database: data model and queries	Lecture and tutorial (3 hr)
Week 07	Graph database: storage and processing	Lecture and tutorial (3 hr)
Week 08	Key-value storage	Lecture and tutorial (3 hr)
Week 09	Spatial Model and Query	Lecture and tutorial (3 hr)
Week 10	Time Series Database	Lecture and tutorial (3 hr)
Week 11	Cloud Database	Lecture and tutorial (3 hr)
Week 12	Scalable SQL	Lecture and tutorial (3 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

Closing the loop

Adjusted the content to fit in 12 week semester. Assessment package has been redesigned to follow university guideline. In particular, we replace quizzes with 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.

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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

Details

Enrolment rules

Teaching staff and contact details

Assessment

Assessment criteria

Late submission

Special consideration

Academic integrity

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Graduate qualities

Outcome map

Closing the loop

Additional information

Disclaimer

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