Subject Descriptions - Subject Information


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


Subject Information
Subject Code ISIT912
Subject Name Big Data Management
Credit Points 6
Pre-Requisites CSIT815
Co-Requisites None.
Restrictions None.
Equivalence None.
Assessment Assessment Tasks: 25% Class Test: 15% Final Exam: 60%
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  
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 hrs lect & 2 hrs lab. No labs in week 1.
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. Demonstrate an understanding of a concept of Big Data, issues related to design management, processing, and infrastructure requirements for implementation of Big Data systems and technologies required for processing of very large data sets.
2. Demonstrate an understanding of scope and functionality of data warehousing systems and use business intelligence software for implementation of data warehousing systems.
3. Demonstrate an understanding of the principles of online analytical data processing (OLAP) and the applications of OLAP techniques in implementation of data warehousing systems.
4. Design and implement data warehousing systems.
5. Demonstrate an understanding of differences between centralized data warehousing systems and processing of large data sets on clustered and distributed computing systems.
6. Design and implement real time data analytical applications on clustered and distributed computing systems.
7. Demonstrate an understanding of other technologies available for implementation data analytical applications including those targeting large unstructured data. Evaluate the suitability of the various technologies to a given commercial setting.


Textbook Information

Text book information is available via the UniShop website:



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