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Stream Computing and Applications (COMP90056)
Graduate courseworkPoints: 12.5On Campus (Parkville)
For information about the University’s phased return to campus and in-person activity in Winter and Semester 2, please refer to the on-campus subjects page.
About this subject
- Overview
- Eligibility and requirements
- Assessment
- Dates and times
- Further information
- Timetable(opens in new window)
Contact information
Semester 2
Tony Wirth
Please refer to the LMS for up-to-date subject information, including assessment and participation requirements, for subjects being offered in 2020.
Overview
Availability | Semester 2 |
---|---|
Fees | Look up fees |
AIM
With exponential growth in data generated from sensor data streams, search engines, spam filters, medical services, online analysis of financial data streams, and so forth, there is demand for fast monitoring and storage of huge amounts of data in real-time. Traditional technologies were not aimed to such fast streams of data. Usually they required data to be stored and indexed before it could be processed.
Stream computing was created to tackle those problems that require processing and classification of continuous, high volume of data streams. It is highly used on applications such as Twitter, Facebook, High Frequency Trading and so forth.
This subject will focus on the algorithms and data structures behind the analysis and management of streams. Theoretical underpinnings are emphasized, with implementation of some fundamental algorithms.
INDICATIVE CONTENT
- Why stream processing is important
- Hash functions, probability, and fundamental data structures
- Data stream model
- Data stream algorithms: Sampling, sketching, distinct items, frequent items, frequency moments, etc.
- Data stream mining: clustering, histograms, query tracking
- Graph streams: connectivity, matchings, covers
Intended learning outcomes
On completion of this subject the student is expected to:
- Design streaming algorithms and data structures for fundamental problems and variants
- Conduct mathematical analysis of such algorithms and data structures
- Implement efficient schemes for streamed large data sets
- Reason about, contrast and compare streaming methods with those for random-access, disk-bound, and parallel computation
Generic skills
On completion of this subject students should have the following skills:
- Ability to apply knowledge of science and engineering fundamentals
- Ability to communicate effectively, with the team and with the community at large
- Capacity for lifelong learning and professional development
- Profound respect for truth and intellectual integrity, and for the ethics of scholarship
Last updated: 3 November 2022
Eligibility and requirements
Prerequisites
Both of:
Code | Name | Teaching period | Credit Points |
---|---|---|---|
COMP90038 | Algorithms and Complexity |
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
|
12.5 |
COMP90041 | Programming and Software Development |
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
|
12.5 |
OR one of:
Code | Name | Teaching period | Credit Points |
---|---|---|---|
COMP20003 | Algorithms and Data Structures | Semester 2 (On Campus - Parkville) |
12.5 |
COMP20007 | Design of Algorithms | Semester 1 (On Campus - Parkville) |
12.5 |
OR entry to MC-IT 100 point program
Corequisites
None
Non-allowed subjects
None
Recommended background knowledge
C or Java programming
Inherent requirements (core participation requirements)
The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.
Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home
Last updated: 3 November 2022
Assessment
Due to the impact of COVID-19, assessment may differ from that published in the Handbook. Students are reminded to check the subject assessment requirements published in the subject outline on the LMS
Description | Timing | Percentage |
---|---|---|
One quiz. Addressing Intended Learning Outcomes (ILOs) 1 and 2.
| Week 4 | 5% |
Two written assignments. Addressing ILOs 1, 2, 3 and 4. Due in weeks 7 and 11.
| From Week 7 to Week 11 | 25% |
One 3 hour end of semester examination. The examination addresses ILOs 1, 2, 3 and 4.
| End of semester | 70% |
Additional details
Hurdle Requirement:
50% Hurdle on exam component and total of non-exam component.
Last updated: 3 November 2022
Dates & times
- Semester 2
Principal coordinator Tony Wirth Mode of delivery On Campus (Parkville) Contact hours 36 hours (1 two-hour lecture per week and 1 one-hour tutorial/lab per week) Total time commitment 200 hours Teaching period 3 August 2020 to 1 November 2020 Last self-enrol date 14 August 2020 Census date 21 September 2020 Last date to withdraw without fail 16 October 2020 Assessment period ends 27 November 2020 Semester 2 contact information
Tony Wirth
Time commitment details
200 hours
Last updated: 3 November 2022
Further information
- Texts
Prescribed texts
“Data Stream Management”, Garofalakis, Minos, Gehrke, Johannes, Rastogi, Rajeev (Eds.), Springer 2016 Recommended only.
- Subject notes
LEARNING AND TEACHING METHODS
The subject involves 1 two-hour lecture per week followed by a 1-hour workshop. Weekly, workshop problems are assigned and discussed during workshop hour. As the subject relies heavily on learning by practice, we have a good load of programming exercises as part of workshops and assignments. Students will work individually or on groups of two to implement algorithms and problems described during lectures and in the workshop.
INDICATIVE KEY LEARNING RESOURCES
The subject uses online reading materials (provided as recommend readings weekly) and online discussion forum. It offers access to slides, book chapters and relevant papers.
CAREERS /INDUSTRY LINKS
Stream processing is becoming more important as the world goes instrumented. Collecting and analysing data became easier and cheaper. One can access the importance of stream processing by looking the number of stream processing platforms being created recently. Stream processing is a key part of the Massive Data Analytics trend.
- Related Handbook entries
This subject contributes to the following:
Type Name Course Master of Science (Computer Science) Course Master of Computer Science Specialisation (formal) Computing Specialisation (formal) Distributed Computing Specialisation (formal) Software - Available through the Community Access Program
About the Community Access Program (CAP)
This subject is available through the Community Access Program (also called Single Subject Studies) which allows you to enrol in single subjects offered by the University of Melbourne, without the commitment required to complete a whole degree.
Entry requirements including prerequisites may apply. Please refer to the CAP applications page for further information.
Additional information for this subject
Subject coordinator approval required.
- Available to Study Abroad and/or Study Exchange Students
This subject is available to students studying at the University from eligible overseas institutions on exchange and study abroad. Students are required to satisfy any listed requirements, such as pre- and co-requisites, for enrolment in the subject.
Last updated: 3 November 2022