Trajectory Data Mining: Algorithmic Problems and Theoretical Studies

PhD Qualifying Examination


Title: "Trajectory Data Mining: Algorithmic Problems and Theoretical Studies"

by

Mr. Haoqiang HUANG


Abstract:

The advances of location-acquisition and mobile devices generate massive 
trajectory data. Many techniques for mining trajectory data have been proposed 
in the past decades and foster a variety of applications. Broadly speaking, 
trajectory data mining can be split into three steps: trajectory preprocessing, 
trajectory indexing and retrieval and data mining tasks. To accelerate these 
steps and get better mining results, many algorithmic problems, including 
trajectory compression, trajectory segmentation, map matching, trajectory 
management and pattern mining, have been studied. Different techniques have 
been utilized to cater for different similarity measures. This survey mainly 
covers the theoretical studies on these algorithmic problems. Some possible 
directions for future research are also discussed.


Date:  			Monday, 23 May 2022

Time:                  	2:00pm - 4:00pm

Zoom Meeting: 
https://hkust.zoom.us/j/97942605560?pwd=Ynl5eE1Kclh5aTNSWHVoMDBpcWVSUT09

Committee Members:	Prof. Siu-Wing Cheng (Supervisor)
 			Prof. Dimitris Papadias (Chairperson)
 			Dr. Sunil Arya
 			Prof. Ke Yi


**** ALL are Welcome ****