University of Technology Sydney

32567 Business Intelligence for Decision Support

Warning: The information on this page is indicative. The subject outline for a particular session, location and mode of offering is the authoritative source of all information about the subject for that offering. Required texts, recommended texts and references in particular are likely to change. Students will be provided with a subject outline once they enrol in the subject.

Subject handbook information prior to 2022 is available in the Archives.

UTS: Information Technology: Computer Science
Credit points: 6 cp

Subject level:

Postgraduate

Result type: Grade and marks

There are course requisites for this subject. See access conditions.

Recommended studies: it is assumed that students are familiar with basic information system concepts and have basic software, database and mathematical skills

Description

Business intelligence is an umbrella term that combines architectures, tools, databases, analytical tools, applications and methodologies. The major objectives of business intelligence is to enable interactive access to data and to give business managers the ability to conduct analysis and make better decisions. Decision support systems are computer-based information systems that combine models/methods and data in an attempt to solve semi/non-structured decision problems with extensive user involvement through a friendly user interface. Business high-level decisions are often semi/non-structured and require an increased level of intelligent and technical support, in particular, when they become rich in data. Decision support systems can be integrated with variable business intelligence techniques to support related decision problem solving. This subject introduces business intelligence, decision support systems, group decision support, intelligent decision support, web-based support systems, decision optimisation technologies, personalised recommender systems. The subject also covers how to design, implement and integrate business intelligence techniques with methods to support business decision-making.

Subject learning objectives (SLOs)

Upon successful completion of this subject students should be able to:

1. Identify key elements of the body of knowledge for the field of decision support technology and business intelligence
2. Identify main decision models, decision methods, and decision support systems
3. Apply decision support models, methods and systems in related business intelligence systems such as e-government, e-business, e-banking, e-logistics, e-learning and warning systems
4. Apply current issues in intelligent decision support systems, multi-criteria, multi-objective and multi-level decision support models, optimization theory, group decision-making models, computational intelligence (such as fuzzy logic), and their applications to business intelligence systems
5. Develop an in-depth understanding of selected parts of the material
6. Communicate in the form of technical reports and presentations
7. Work in a group to achieve a common objective and task

Course intended learning outcomes (CILOs)

This subject also contributes specifically to the development of the following Course Intended Learning Outcomes (CILOs):

  • Socially Responsible: FEIT graduates identify, engage, and influence stakeholders, and apply expert judgment establishing and managing constraints, conflicts and uncertainties within a hazards and risk framework to define system requirements and interactivity. (B.1)
  • Design Oriented: FEIT graduates apply problem solving, design thinking and decision-making methodologies in new contexts or to novel problems, to explore, test, analyse and synthesise complex ideas, theories or concepts. (C.1)
  • Technically Proficient: FEIT graduates apply theoretical, conceptual, software and physical tools and advanced discipline knowledge to research, evaluate and predict future performance of systems characterised by complexity. (D.1)
  • Collaborative and Communicative: FEIT graduates work as an effective member or leader of diverse teams, communicating effectively and operating autonomously within cross-disciplinary and cross-cultural contexts in the workplace. (E.1)
  • Reflective: FEIT graduates critically self-review their own and others' performance with a high level of responsibility to improve and practice competently for the benefit of professional practice and society. (F.1)

Teaching and learning strategies

Three-hour sessions per week of integrated presentations.

The subject will consist of approximately 18 hours of lectures and 21 hours of seminar/workshops supplemented by guided online/offline discussion. These sessions may be offered on a weekly basis throughout the session or in block mode.

Content (topics)

  1. Decision making and business intelligence
  2. Business decision models and methods
  3. Decision support system and its development
  4. Group decision support and business performance evaluation
  5. Multi-criteria decision-making and its business applications
  6. Intelligent and cognition-driven process for business intelligence
  7. Personalised recommender systems for business intelligence
  8. Information integration for business intelligence
  9. Web-based decision support technology in business intelligence
  10. Soft computation (such as fuzzy logic) application in business intelligence
  11. Advanced intelligent decision and business intelligence techniques

Assessment

Assessment task 1: Discussion post

Intent:

To promote online engagement and discussion with peers around key idea in decision support.

Objective(s):

This assessment task addresses the following subject learning objectives (SLOs):

6 and 7

This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs):

E.1

Groupwork: Individual
Weight: 5%

Assessment task 2: Essay

Intent:

To explore and understand current techniques in decision support.

Objective(s):

This assessment task addresses the following subject learning objectives (SLOs):

1, 2, 3, 4, 5 and 6

This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs):

B.1, C.1, D.1, E.1 and F.1

Type: Essay
Groupwork: Individual
Weight: 30%
Length: Approximately 2500 words

Assessment task 3: Class individual presentation

Intent:

To present an oral summary of findings and respond to relevant questions from peers and experts.

Objective(s):

This assessment task addresses the following subject learning objectives (SLOs):

1, 2, 4, 5 and 6

This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs):

C.1, D.1 and E.1

Type: Presentation
Groupwork: Individual
Weight: 15%
Length:

About 15 minutes

Assessment task 4: Research project

Intent:

To explore new and emerging problems in decision support , find effective models, and provide solution to support decision making.

Objective(s):

This assessment task addresses the following subject learning objectives (SLOs):

1, 2, 3, 4, 5, 6 and 7

This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs):

B.1, C.1, D.1, E.1 and F.1

Type: Project
Groupwork: Group, group assessed
Weight: 30%

Assessment task 5: Presentation

Intent:

To present problem findings and explain process to audience of peers and experts.

Objective(s):

This assessment task addresses the following subject learning objectives (SLOs):

1, 2, 3, 4, 5, 6 and 7

This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs):

C.1, D.1 and E.1

Type: Presentation
Groupwork: Group, individually assessed
Weight: 20%
Length:

About 20 minutes (including questions)

Minimum requirements

In order to pass the subject, a student must achieve an overall mark of 50% or more.

Recommended texts

Efraim Turban, Jay Aronson (2007), Decision support systems and intelligent systems, sixth edition, Prentice Hall.

Gupta, J.N., Forgionne, G.A. and Mora, M. eds. (2007), Intelligent decision-making support systems: foundations, applications and challenges. Springer Science & Business Media.

Lu, J., Zhang, G., Ruan, D. and Wu, F. (2007), Multi-objective group decision-making: methods, software and applications, Imperial College Press, London.

References

George Marakas, Decision support systems in 21st century, second edition, Prentice Hall

Bernard Liautaud, E-Business Intelligence, McGraw Hill,2000

Some papers from the journal of Decision support systems, Computer and industry engineering, Information and management, and some articles given in classes.

Other resources

Canvas may be used to distribute course material, announcements and facilitate discussions, located at https://canvas.uts.edu.au/