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 viewed from different 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 sufficient 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 prefix tree-like different ways of different 
methodologies. For example, the line simplification techniques mainly deal 
with the curvilinear features of the data, while map-matched compression 
techniques focus on graph theory based problems like shortest path. Different 
aspects will have their pros and cons respectively. In this survey, we bring 
a deep insight into the field of spatio-temporal data compression by giving a 
classification 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


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