Note: This is an archived Handbook entry from 2015.
|Dates & Locations:|| |
This subject has the following teaching availabilities in 2015:Semester 1, Parkville - Taught on campus.
Timetable can be viewed here. For information about these dates, click here.
|Time Commitment:||Contact Hours: 36 hours |
Total Time Commitment:
This subject is only available to Research Higher Degree students.
Undergraduate level subjects in programming (such as C/C++ or Java), databases, and statistics are required.
Students may enrol in this subject only with the approval of the subject coordinator; a brief discussion with the subject coordinator is required before enrolment.
|Recommended Background Knowledge:||None|
|Non Allowed Subjects:||None|
|Core Participation Requirements:||
For the purposes of considering request for Reasonable Adjustments under the Disability Standards for Education (Cwth 2005), and Student Support and Engagement Policy, academic requirements for this subject are articulated in the Subject Overview, Learning Outcomes, Assessment and Generic Skills sections of this entry.
It is University policy to take all reasonable steps to minimise the impact of disability upon academic study, and reasonable adjustments will be made to enhance a student's participation in the University's programs. Students who feel their disability may impact on meeting the requirements of this subject are encouraged to discuss this matter with a Faculty Student Adviser and Student Equity and Disability Support: http://services.unimelb.edu.au/disability
Associate Professor Rui Zhang
As the proliferation of mobile devices continues, spatiotemporal (ST) data is produced at unprecedented speed. We can mine interesting knowledge from this data for wide ranges of applications such as traffic management, route optimisation, location-based social networks, disaster monitoring and urban planning. Faced with a plethora of emerging and novel applications on ST data, what knowledge to mine from the data, how to mine the data and how to manage the huge amount of data are pressing issues. This subject provides an introduction to such topics and focuses on a few advanced topics such as indexing and query processing ST data, probabilistic models for ST data, spatial keyword search, and spatio-textual data join queries. This subject provides students with the basis and background to enter more advanced research in this exiting area.
INTENDED LEARNING OUTCOMES (ILOs)
Having completed this subject the students are expected to:
Hurdle Requirement: To pass the subject, students must obtain at least:
|Breadth Options:|| |
This subject is not available as a breadth subject.
|Fees Information:||Subject EFTSL, Level, Discipline & Census Date|
On completing this subject, students should be able to:
Master of Philosophy - Engineering |
Download PDF version.