ISYS4450: Semester 1, 2024

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Units ISYS4450 Semester 1 2024 [Normal evening]

Unit of study_

ISYS4450: Knowledge Management Systems

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

Overview

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

Unit code	ISYS4450
Academic unit	Computer Science
Credit points	6
Prohibitions ?	ISYS5050
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
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

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:
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:
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:
= 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.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

Learning support

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

Weekly schedule

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

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

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.

Outcomes
Graduate qualities

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
Learning outcomes

Responding to student feedback

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

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

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