COMP SCI 4094 - Distributed Databases and Data Mining
North Terrace Campus - Semester 1 - 2019
General Course Information
Course Code COMP SCI 4094 Course Distributed Databases and Data Mining Coordinating Unit School of Computer Science Term Semester 1 Level Undergraduate Location/s North Terrace Campus Units 3 Contact Up to 2 hours per week Available for Study Abroad and Exchange Y Prerequisites COMP SCI 2201 Incompatible COMP SCI 4194 Assumed Knowledge Knowledge of database systems as taught in COMP SCI 2207 Course Description Topics covered in this course include: Distributed database system architecture, Distributed database system design, Distributed query processing and optimisation, Distributed transaction management, Data warehousing and OLAP technology, Association analysis, Classification and prediction, Cluster analysis, Mining complex types of data.
Course Coordinator: Adjunct Professor Hong Shen
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning OutcomesOn completion of this course, students should:
- Explain distributed database systems architecture and design
- Apply methods and techniques for distributed quey processing and optimisation
- Explain the broad concepts of distributed transaction process
- Discuss the basic concepts of Data warehousing and OLAP technology
- Apply methods and techniques for association analysis, data classification and clustering
University Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
University Graduate Attribute Course Learning Outcome(s) Deep discipline knowledge
- informed and infused by cutting edge research, scaffolded throughout their program of studies
- acquired from personal interaction with research active educators, from year 1
- accredited or validated against national or international standards (for relevant programs)
1-5 Critical thinking and problem solving
- steeped in research methods and rigor
- based on empirical evidence and the scientific approach to knowledge development
- demonstrated through appropriate and relevant assessment
1-5 Teamwork and communication skills
- developed from, with, and via the SGDE
- honed through assessment and practice throughout the program of studies
- encouraged and valued in all aspects of learning
2,5 Career and leadership readiness
- technology savvy
- professional and, where relevant, fully accredited
- forward thinking and well informed
- tested and validated by work based experiences
1-5 Intercultural and ethical competency
- adept at operating in other cultures
- comfortable with different nationalities and social contexts
- able to determine and contribute to desirable social outcomes
- demonstrated by study abroad or with an understanding of indigenous knowledges
1,3,4 Self-awareness and emotional intelligence
- a capacity for self-reflection and a willingness to engage in self-appraisal
- open to objective and constructive feedback from supervisors and peers
- able to negotiate difficult social situations, defuse conflict and engage positively in purposeful debate
Required ResourcesText 1:
M. T. Oszu and P. Valduriez, Principles of Distributed Database Systems, 2nd ed.,
Prentice-Hall, 1999. Errata
J. Han and M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann, 2000. Errata
Recommended ResourcesAdditional materials posted on the course homepage:
Learning & Teaching Activities
Learning & Teaching ModesLectures, programming assignments, class questions
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.6 hours per week
Learning Activities SummaryLectures, programming assignments, class questions
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Assessment SummaryProgramming assignments, final exam
Assessment Related RequirementsIndividual programming assignments
Individual final exam (closed book)
Assessment DetailProgramming assignments: 30%
Final exam: 70%
SubmissionProgramming assignments: online via web submission system
Final exam: University certrally managed
Grades for your performance in this course will be awarded in accordance with the following scheme:
M10 (Coursework Mark Scheme) Grade Mark Description FNS Fail No Submission F 1-49 Fail P 50-64 Pass C 65-74 Credit D 75-84 Distinction HD 85-100 High Distinction CN Continuing NFE No Formal Examination RP Result Pending
Further details of the grades/results can be obtained from Examinations.
Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.
Final results for this course will be made available through Access Adelaide.
The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.
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- Reasonable Adjustments to Teaching & Assessment for Students with a Disability Policy
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