Subject Descriptions - Subject Information


Calendar: 2017 Undergraduate
Faculty: Faculty of Engineering and Information Sciences
Department: School of Computing and Information Technology


Subject Information
Subject Code ISIT312
Subject Name Big Data Management
Credit Points 6
Pre-Requisites CSIT115 and 24cp @ 100 level CSIT
Co-Requisites None.
Restrictions None.
Equivalence None
Assessment  
General Subject Yes.
EFTSL (Non Weighted) 0.125
Non Weighted Student Contribution Amounts
Commonwealth Supported (HECS) Students Only
Pre-1997 Pre-2005 Post-2005 Post-2008 Post-2009 Post-2010
$ 1131  $ 1131  $ 1131  $ 1131  $ 1131  $ 1131 
Weighted Student Contribution Amounts
Commonwealth Supported (HECS) Students Only
Course
1771-Bachelor of Laws (Honours) (Direct Entry)
1777-Bachelor of Laws (Direct Entry)
1827-Bachelor of International Studies - Bachelor of Laws
1845-Bachelor of Information Technology - Bachelor of Laws
1852-Bachelor of Business Information Systems - Bachelor of Laws
329-Bachelor of Economics and Finance-Bachelor of Laws
336-Bachelor of Science (Psychology) - Bachelor of Laws
340-Bachelor of Arts - Bachelor of Laws
351-Bachelor of Laws (Honours)
760-Bachelor of Communication and Media Studies - Bachelor of Laws
770-Bachelor of Laws (Graduate Entry)
771-Bachelor of Arts - Bachelor of Laws
771H-Bachelor of Arts - Bachelor of Laws
772-Bachelor of Creative Arts - Bachelor of Laws
773-Bachelor of Commerce - Bachelor of Laws
774-Bachelor of Mathematics - Bachelor of Laws
775-Bachelor of Science - Bachelor of Laws
775H-Bachelor of Science - Bachelor of Laws
775M-Course information not Found
776-Bachelor of Computer Science - Bachelor of Laws
778-Bachelor of Information and Communication Technology-Bachelor of Laws
779-Bachelor of Engineering - Bachelor of Laws
858-Bachelor of Journalism - Bachelor of Laws
Work Experience No
Tutorial Enrolment Information None.

Subject Availability
Session Spring  (24-07-2017 to 16-11-2017)
Campus Wollongong
Delivery Method On Campus
Instance Name Class 1
Course Restrictions No restrictions
Contact Hours 2 Hour Lec, 2 Hour Lab
Lecturer(s) and
Cons. times
Guoxin Su
Janusz Getta
Coordinator(s) and
Cons. times
Guoxin Su
Janusz Getta
Instance Comment  
Census Date 31-08-2017

Subject Description
The subject addresses the problems of managing and processing of extremely large data sets in a single-server centralized computing systems and in multi-server clustered and distributed computing systems. The topics related to processing of large data sets in centralized environments include the techniques based on the classical data warehouse technologies such multidimensional data model, data warehouse architecture, data warehouse design both at conceptual and logical levels, and data warehouse processing with appropriate specialised query operations. The topics related to processing of large data sets in distributed environments include the techniques that can be implemented on the clusters of inexpensive computing nodes using MapReduce programming model. The subject introduces the students to the real time analytical processing of large data sets with analytical cluster-based distributed data processing systems. Discussion and hands on exercises related to these topics will equip students to meet the challenges in Big Data environments and appreciate the added challenges of dealing with unstructured data. Students will be presented with opportunities to do hands-on work with appropriate commercial tools.


Subject Learning Outcomes
On successful completion of this subject, students will be able to:
1. Formulate the principles of Big Data and summarise the issues related to design management, processing, and infrastructure requirements for implementation of Big Data systems and technologies .
2. Summarise the required functionalities of data warehousing systems and application of business intelligence software for implementation of data warehousing systems.
3. Summarize the principles of online analytical data processing (OLAP) and the applications of OLAP techniques in implementation of data warehousing systems.
4. Design and create data warehousing systems.
5. Compare centralized data warehousing systems with processing of large data sets on clustered and distributed computing systems.
6. Design and create real time data analytical applications on clustered and distributed computing systems.
7. Evaluate the other technologies available for implementation data analytical applications including those targeting large unstructured data.


Textbook Information

Text book information is available via the UniShop website:



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