Management and Mining of Spatio-Temporal Data

1. Management and Mining of Spatio-Temporal Data Rui Zhang http://www.csse.unimelb.edu.au/~rui The University of Melbourne

2. Subject Information • Topic: Management and Mining of Spatio-Temporal Data • Form: summer intense subject • Subject code and name: COMP90005 Advanced Studies in Computing 6B • No LMS website. Subject homepage: http://ww2.cs.mu.oz.au/~rui/spatio-temporal.htm • No textbook, all materials from subject homepage • Time: three weeks

3. Objective • Introduction to spatio-temporal data management and mining • Basics of databases, queries, indexes • Spatial queries and indexes • Spatio-temporal queries and indexes • Location-based social networks • Graphs, basics on graph mining algorithms • Cloud computing and MapReduce • Trajectory data management and mining, trajectory privacy • Skills for understanding advanced research papers, writing top conference/journal papers, and paper reviewing in this area • Outcome • Knowledge and ideas about the above topics • Ability to read (large) research code and modify the code for your own use • Ability to understand key quality indicators of research papers and write reviews on such papers • Who is subject for: • PhD students • Master-by-research students • Master-by-course students who are interested in doing research to obtain basics to enter more advanced research in this exiting area.

4. More Subject Information • Feature • First time offered, may or may not be offered again • Guest lecture every other day – Academics from our department • Interactive and exploratory learning – nature of research, sooooo don’t be upset if things are not perfect.  • Provisional schedule (see website); tolerance • Enrolment / Withdraw • Those who are not enrolled: we will not mark any assignments or report from you, so please do NOT submit them. • Contact: • 9 days: 3 hours lecture + 1 hour lab on most days • Total 36 contact hours, but expect a workload of 120 hours. • Because this is an intense subject, you might want to spend some after-hours • Expectation • Come to lectures and actively participate in class discussions • Do all the assignments and reports by yourself • Work hard in these three weeks

5. Assessment • Two lab assignments: 30 marks • Assignment 1: Spatial queries Due Friday of 1st week • Assignment 2: MapReduce Due Friday of 2nd week • Challenge queries from both due together with Final Report • Proposal of a data structure, 500 words: 10 marks • Must submit a draft describing the idea: Due Thursday of 2nd week and feedback will be provided to you • Final proposal of data structure due as part of Final Report • Presentation of reviewing one assigned paper: 15 marks • Group of three students presenting the paper itself and your review on the paper, on Monday of 3rd week. • Paper review assignments: 45 marks • Due as part of Final Report • To pass the subject, you must get at least • Lab assignments:            12 out of 30 • Data structure proposal:   5 out of 10 • Presentation:                   7 out of 15 • Overall:                         50 out of 100 NOTE: all assignments and reports are individual work except for the presentation of reviewed paper.

6. Academic Misconduct Workload Try to plagiarize Do it by yourself 0 25% 50% 75% 100% Probability of NOT getting caught