Compression Methods for Spatio-temporal Data Of Moving Objects

PhD Qualifying Examination


Title: "Compression Methods for Spatio-temporal Data Of Moving Objects"

by

Mr. Yudian JI


Abstract:

Advanced location acquisition technologies have led to a wide range of location 
based applications and services supported by location acquisition systems like 
GPS and sensor networks. The popularization of location based applications, in 
turn, has caused the explosion of spatio-temporal data of moving objects, 
consequently making the spatio-temporal data compression techniques desired. 
The spatio-temporal data are data with location information and time series. 
From erent solutions about data compression. Spatio-temporal data of moving 
objects are basically stored as strings, which means traditional string 
compression techniques can be applied for data compression. Traditional string 
compression methods are good in some cases because they are simple and fast. 
However, these methods ignored the spatio-temporal characteristics of data, 
which may lead to a better performance of compression. More importantly, the 
data lose all the utility after compression, thus being very inconvenient for 
those data that need to be queried often. The limitations here make traditional 
compression techniques based on information theory not su Date: cient for the 
cases where optimized compression ratio is important or frequent queries are 
needed. Taking the spatio-temporal characteristics into consideration, there 
are also two ways of looking at the problem. The data could be seen as 
trajectories with geometry instincts, or prexed tree-like erent ways of erent 
methodologies. For example, the line simplication techniques mainly deal with 
the curvilinear features of the data, while map-matched compression techniques 
focus on graph theory based problems like shortest path erent aspects will have 
their pros and cons respectively. In this survey, we bring a deep insight into 
the eld of spatio-temporal data compression by giving a classication of current 
spatio-temporal data compression techniques and reviewing the typical methods 
of each kind. We study their basic ideas, methodologies and evaluations. 
Comments on pros and cons will be given respectively.


Date: 			Friday, 12 June 2015

Time:                  	2:30pm - 4:30pm

Venue:                  Room 3584
                         Lifts 27/28

Committee Members:	Prof. Lionel Ni (Supervisor)
 			Dr. Qiong Luo (Chairperson)
 			Dr. Lin Gu
 			Dr. Ke Yi


**** ALL are Welcome ****