COMP SCI 7403 - Mining Big Data

North Terrace Campus - Semester 2 - 2016

The Web and Internet Commerce provide extremely large datasets from which important information can be extracted by data mining. This course will cover practical algorithms for solving key problems in mining of massive datasets. It focuses on parallel algorithmic techniques that are used for large datasets in the area of cloud computing. Furthermore, stream processing algorithms for data streams that arrive constantly, page ranking algorithms for web search, and online advertisement systems are studied in detail.

  • General Course Information
    Course Details
    Course Code COMP SCI 7403
    Course Mining Big Data
    Coordinating Unit School of Computer Science
    Term Semester 2
    Level Postgraduate Coursework
    Location/s North Terrace Campus
    Units 3
    Contact Up to 2 hours per week
    Available for Study Abroad and Exchange Y
    Prerequisites COMP SCI 7201
    Course Description The Web and Internet Commerce provide extremely large datasets from which important information can be extracted by data mining. This course will cover practical algorithms for solving key problems in mining of massive datasets. It focuses on parallel algorithmic techniques that are used for large datasets in the area of cloud computing. Furthermore, stream processing algorithms for data streams that arrive constantly, page ranking algorithms for web search, and online advertisement systems are studied in detail.
    Course Staff

    Course Coordinator: Dr Markus Wagner

    Course Timetable

    The full timetable of all activities for this course can be accessed from Course Planner.

  • Learning Outcomes
    Course Learning Outcomes
    1. To develop knowledge of algorithms for massive data sets and methodologies in the context of data mining.
    2. To gain experience in matching various algorithms for particular classes of problems.
    3. To gain experience in applying and developing algorithms as a part ofsoftware development for mining big data.
    4. Read and understand scientific research papers in the area of big data, critically evaluate research papers, and present them in a seminar talk.
    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)
    1,2,3,4
    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
    1,2,3,4
    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
    1,2,3
    Career and leadership readiness
    • technology savvy
    • professional and, where relevant, fully accredited
    • forward thinking and well informed
    • tested and validated by work based experiences
    4
    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
    1,2,3
    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
    3,4
  • Learning Resources
    Required Resources
    The textbook for this course is: Anand Rajaraman, Jeffrey Ullman: Mining Massive Datasets, Cambridge University Press, 2012
    Recommended Resources
    During the course, additional literature (available fromInternet) will be recommended as additional reading.
    Online Learning
    The course will use the University's learning management system - Canvas. Students are expected to check the forum on a regular basis for announcements relating to the course and projects.
  • Learning & Teaching Activities
    Learning & Teaching Modes

    No information currently available.

    Workload

    No information currently available.

    Learning Activities Summary

    No information currently available.

  • Assessment

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. Assessment must maintain academic standards.

    Assessment Summary
    3 assignments based on the material presented in the lecture (weight 10% each).
    1 open-ended individual project (weight 70%).

    The due dates will be announced through the course website.
    Assessment Detail
    Assessement Type Weight Learning objectives Abstraction Design Communication Data Programming
    Assignment 1 Formative 10% 1,2,3 4 4 4 5 5
    Assignment 2 Formative 10% 1,2,3 5 5 4 5 5
    Assignment 3 Formative 10% 1,2,3 5 5 4 5 5
    Open-ended project

    Formative,

    Summative

    70% 2,4 5 5 5 5 5

    CBOK categories are explained in section 4 of the ICT core body of knowlege. Numbers assigned correspond to the Bloom taxonomy (see page 26 of the same document).
    Submission
    All work will be submitted through the course forum.
    Course Grading

    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.

  • Student Feedback

    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.

    SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.

  • Student Support
  • Policies & Guidelines
  • Fraud Awareness

    Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student’s disciplinary procedures.

The University of Adelaide is committed to regular reviews of the courses and programs it offers to students. The University of Adelaide therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.