Knowledge Technologies

Subject COMP90049 (2013)

Note: This is an archived Handbook entry from 2013.

Credit Points: 12.50
Level: 9 (Graduate/Postgraduate)
Dates & Locations:

This subject is not offered in 2013.

Time Commitment: Contact Hours: 36 hours, comprising of two 1-hour lectures and one 1-hour workshop per week
Total Time Commitment:

120 hours


One of the following:

Study Period Commencement:
Credit Points:
Not offered in 2013
Not offered in 2013
Not offered in 2013


433-253 Algorithms and Data Structures



Recommended Background Knowledge:


Non Allowed Subjects:


433-352 Data on the Web

Core Participation Requirements:


Professor Rao Kotagiri


Subject Overview:

Much of the world's knowledge is stored in the form of unstructured data (e.g. text) or implicitly in structured data (e.g. databases). In this subject students will learn algorithms and data structures for extracting, retrieving and storing explicit knowledge from various data sources, with a focus on the web. Topics include: data encoding and markup, web crawling, clustering, regular expressions, pattern mining, Bayesian learning, instance-based learning, document indexing, database storage and indexing, and text retrieval.


Having completed this unit the student is expected to apply his knowledge and skills in many fields that need extensive data analysis.

The student would gain skills to describe and apply the fundamentals of knowledge systems, including data acquisition and aggregation, knowledge extraction, text retrieval, machine learning and data mining in many application domains ranging from commerce to medicine.

  • Project work during semester, expected to take about 36 hours (40%). Project 1 will starts at the beginning of the 5th week and project is for 3 weeks. Project 2 commences on the 8th week and finishes on 12th week.

These projects will be oriented to attain ILO 1-2 and all General skills. Students will develop their own code and use standard libraries for text processing and classification. They have to analyse the results and document system and the analysis performed.

  • One mid-semester test (10%): This test will assess students understanding related to text processing and evaluations methods for answer sets of queries. The test is conducted during the 5th week.
  • One 2-hour examination held during the examination period (50%). This is a comprehensive test to assesses students understanding of all the topics covered in the subject.

To pass the subject, students must obtain at least:50% overall 20/40 in project work, and 30/60 in the mid-semester test and end-of-semester written examination combined

Prescribed Texts:


Breadth Options:

This subject is not available as a breadth subject.

Fees Information: Subject EFTSL, Level, Discipline & Census Date
Generic Skills:

General skills include the ability to undertake problem identification, formulation and developing solutions especially exploiting acquired data. In addition, this subject exposes students to use various data processing tools and make them learn integration of these tools to build more complex software systems.

As a result, the student will develop skills to utilise a systems approach to complex problems. They also develop skills to manage information and produce documentation of the developed system. The project work associated with the course creates ample scope for creativity, originality and innovation.

Related Course(s): Master of Engineering in Distributed Computing
Master of Information Technology
Master of Information Technology
Master of Information Technology
Master of Information Technology
Master of Philosophy - Engineering
Master of Science (Computer Science)
Master of Software Systems Engineering
Ph.D.- Engineering
Related Majors/Minors/Specialisations: Computer Science
Master of Engineering (Software)

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