COMP SCI 4403 - Mining Big Data

North Terrace Campus - Semester 1 - 2014

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 4403
    Course Mining Big Data
    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
    Prerequisites COMP SCI 2201
    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 Frank Neumann

    Course Timetable

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

  • Learning Outcomes
    Course Learning Outcomes
    To develop knowledge of algorithms for massive data sets
    and methodologies in the context of data mining.
    To gain experience in matching various algorithms for particular classes of problems.
    To gain experience in applying and developing algorithms as a part of
    software development for mining big data.
    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

    No information currently available.

  • 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 from
    Internet) will be recommended as additional reading.



    Online Learning
    The course will use a
    Moodle forum; 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
    1 seminar and 1 essay based on a research paper at a recent leading international conference
    Assessment Detail

    No information currently available.

    Submission

    No information currently available.

    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

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    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

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