Note: This is an archived Handbook entry from 2016.
|Dates & Locations:|| |
This subject is not offered in 2016.
|Time Commitment:||Contact Hours: 36 hours (1 two-hour lecture per week and 1 one-hour tutorial/lab per week) |
Total Time Commitment:
Study Period Commencement:
Semester 1, Semester 2
|Recommended Background Knowledge:|| |
Java programming language, design of algorithms, distributed systems
|Non Allowed Subjects:|| |
|Core Participation Requirements:||
For the purposes of considering request for Reasonable Adjustments under the Disability Standards for Education (Cwth 2005), and Student Support and Engagement Policy, academic requirements for this subject are articulated in the Subject Overview, Learning Outcomes, Assessment and Generic Skills sections of this entry.
It is University policy to take all reasonable steps to minimise the impact of disability upon academic study, and reasonable adjustments will be made to enhance a student's participation in the University's programs. Students who feel their disability may impact on meeting the requirements of this subject are encouraged to discuss this matter with a Faculty Student Adviser and Student Equity and Disability Support: http://services.unimelb.edu.au/disability
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.
The Stream computing course will interest students who want to learn more about real-time processing and its applications. It will be taught both from a theoretical and practical point of view. The course will cover underlying fundamentals of stream processing systems, particularly architectural issues and algorithms for stream processing, mining and analysis. It will also include tutorials on how to develop and deploy applications into platforms such as IBM InfoSphere Streams®.
INTENDED LEARNING OUTCOMES (ILO)
On completion of this subject the student is expected to:
Intended Learning Outcomes (ILOs) 1 and 2 are addressed in all components of assessment. ILO 3 and 4 are assessed in the end of semester exam and the final project.
|Prescribed Texts:|| |
|Breadth Options:|| |
This subject is not available as a breadth subject.
|Fees Information:||Subject EFTSL, Level, Discipline & Census Date|
On completion of this subject students should have the following skills:
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.
Master of Information Technology |
Master of Information Technology
MIT Computing Specialisation |
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