Knowledge Technologies

Subject COMP90049 (2014)

Note: This is an archived Handbook entry from 2014.

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

This subject is not offered in 2014.

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

200 hours


One of the following:

Study Period Commencement:
Credit Points:
Semester 1, Semester 2


433-253 Algorithms and Data Structures



Recommended Background Knowledge:


Non Allowed Subjects:


433-352 Data on the Web

Core Participation Requirements:

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.

Introduction to Knowledge Technologies; String search; Spelling; matching; Genomics; Text processing and search; Web search; Introduction to Data Mining; Introduction to basic; introduction to basic Probability; Data mining Issues; Classification; Association Rules; Clustering

Learning Outcomes:


On completion of this subject the student is expected to:

  1. To apply knowledge and skills in many fields that need extensive data analysis
  2. 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 Intended Learning Outcomes (ILOs) 1 and 2 and all Generic 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

Hurdle requirement: 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:

On completion of this subject, students should have the following 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


This course is taught over 12 weeks, each week with two one hour formal lectures and a one hour workshop. During the workshops the students are given problems to solve to reinforce the previous week’s lecturing material. The problem solving nature of the workshops is geared for the students to learn and understand the concepts of the subject material.


Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schutze (2008), Information Retrieval, Cambridge University Press. Freely available at

Pang-Ning Tan, Michael Steinbach and Vipin Kumar (2005) Introduction to Data Mining, Addison-Wesley.


This subject is relevant to many fields including Engineering, Commerce, Government Organizations, Research Institutes and Institutions in Medicine where data analysis can play a significant improvement in delivering services or improving profits.

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 with Business)
Master of Engineering (Software)

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