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Unit outline_

ISYS4450: Knowledge Management Systems

Semester 1, 2024 [Normal evening] - Camperdown/Darlington, Sydney

The need to track and facilitate the sharing of the core knowledge resources in contemporary organisations is widely recognised. This course will provide a comprehensive introduction to the area of Knowledge Management (KM) from both technological and organisational perspectives. We will review and discuss a range of published papers, case studies, and other publications that deal with a range of important KM-related topics. One of the key knowledge management technologies, Business Intelligence Systems, will be covered in detail. It will also include hands-on work using the BI (Online Analytical Processing- OLAP) tool, COGNOS. Some of the main themes to be covered will include: KM- Conceptual Foundations; Taxonomies of organizational knowledge and KM mechanisms; Case/Field Studies of KM Initiatives; Data Warehousing and OLAP/Business Analytics; Data, text, and web mining; Social media, crowdsourcing, and KM; Big data and actionable knowledge.

Unit details and rules

Academic unit Computer Science
Credit points 6
Prerequisites
? 
ISYS2160
Corequisites
? 
Enrolment in a thesis unit. INFO4001 or INFO4911 or INFO4991 or INFO4992 or AMME4111 or BMET4111 or CHNG4811 or CIVL4022 or ELEC4712 or COMP4103 or SOFT4103 or DATA4103 or ISYS4103
Prohibitions
? 
ISYS5050
Assumed knowledge
? 

None

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Simon Poon, simon.poon@sydney.edu.au
Lecturer(s) Rouzbeh Meymandpour, rouzbeh.meymandpour@sydney.edu.au
The census date for this unit availability is 2 April 2024
Type Description Weight Due Length
Supervised exam
? 
Final exam
Exam will cover lectures, tutorials and the final project
50% Formal exam period 2 hours
Outcomes assessed: LO2 LO3 LO4
Assignment In-Class Quiz
Will cover the materials covered in the lectures till Week 6
15% Mid-semester break
Due date: 10 Apr 2024 at 23:59
1 hour
Outcomes assessed: LO2 LO3 LO4 LO6
Assignment group assignment Data analytics (OLAP) assignment
Group project work; real world problem solving; report preparation.
35% Week 13
Due date: 22 May 2024 at 23:59
N/A
Outcomes assessed: LO1 LO2 LO4 LO5 LO6
group assignment = group assignment ?

Assessment summary

  • Small Test: In-Class Quiz
  • Assignment: Group Project Report
  • Final Exam: Supervised Exam

Assessment criteria

Result name

Mark range

Description

High distinction

85 - 100

 

Distinction

75 - 84

 

Credit

65 - 74

 

Pass

50 - 64

 

Fail

0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

Deduction of 5% of maximum mark for each calendar day after the due date. After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy 2023 reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy 2023. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

WK Topic Learning activity Learning outcomes
Week 01 Introduction to Knowledge Management Lecture (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 02 KM, Decision Support Systems and Business Intelligence Lecture (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
ETL – Part 1 Computer laboratory (1 hr) LO1 LO5 LO6
Week 03 BI, ETL, Data Warehouses Lecture (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
ETL – Part 2 Computer laboratory (1 hr) LO1 LO5 LO6
Week 04 Data Warehouses, Data Lakes, OLAP & OLTP Lecture (2 hr) LO1 LO3 LO4 LO5
BI – Introduction Computer laboratory (1 hr) LO1 LO2 LO5 LO6
Week 05 Big Data and New Trends in BI Lecture (2 hr) LO1 LO2 LO3 LO5
BI – Advanced Data Analytics – Part 1 Computer laboratory (1 hr) LO1 LO2 LO5 LO6
Week 06 Data Visualisation, Dashboards and Data Storytelling Lecture (2 hr) LO2 LO4 LO5
BI – Advanced Data Analytics – Part 2 Computer laboratory (1 hr) LO1 LO4 LO5 LO6
Week 07 KM/Web 1.0, 2.0, 3.0 and beyond Lecture (2 hr) LO2 LO3 LO4 LO5
BI – Dashboards Computer laboratory (1 hr) LO1 LO2 LO4 LO5 LO6
Week 08 Semantic Technologies and Knowledge Graphs Lecture (2 hr) LO2 LO3 LO4 LO5
Knowledge Graphs – Part 1 Computer laboratory (1 hr) LO1 LO2 LO5 LO6
Week 09 Knowledge Graph Use Cases, Open vs Enterprise Lecture (2 hr) LO3 LO4 LO5
Knowledge Graphs – Part 2 Computer laboratory (1 hr) LO1 LO4 LO5 LO6
Week 10 Ontologies and Ontology Engineering Lecture (2 hr) LO2 LO3 LO4 LO5
Group Project Work and Discussions Computer laboratory (1 hr) LO1 LO3 LO4 LO5
Week 11 Semantic Data Modelling and Querying Lecture (2 hr) LO2 LO3 LO4 LO5
Group Project Work and Discussions Computer laboratory (1 hr) LO1 LO3 LO4 LO5 LO6
Week 12 Knowledge Creation, Discovery, Inference and Reasoning Lecture (2 hr) LO2 LO3 LO4
Group Project Work and Discussions Computer laboratory (1 hr) LO1 LO3 LO4 LO5 LO6
Week 13 Review and Revision Lecture (2 hr) LO2 LO3 LO4 LO5
Group Project Work and Discussions Computer laboratory (1 hr) LO1 LO3 LO4 LO5 LO6

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. demonstrate effective project and teamwork skills
  • LO2. understand the conceptual foundations and technological underpinnings of KM
  • LO3. critically read the research and related literature on knowledge management
  • LO4. understand the interplay between technical and organisational issues in implementing and using KM systems
  • LO5. conduct an extended requirements analysis of knowledge management tools/systems and be able to identify and monitor changing information and knowledge needs of in the broad domain of knowledge management
  • LO6. provide in-depth analytical reporting of knowledge management tools/systems and convey extensive consideration of theoretical and methodological issues of important knowledge management related topics

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

This section outlines changes made to this unit following staff and student reviews.

No significant changes have been made since this unit was last offered

Disclaimer

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