Visual Summarization and Pattern Exploration for Spatio-temporal Data

PhD Thesis Proposal Defence


Title: "Visual Summarization and Pattern Exploration for Spatio-temporal Data"

by

Mr. Jiansu PU


ABSTRACT:

Nowadays spatiotemporal data are increasingly becoming available to 
researchers due to the advances in technologies like mobile phones, GPS, 
and wireless communication devices. Analyzing such data collected from 
human daily life can shed light into people’s behaviors and thus has high 
social and commercial values. Mining or analyzing these kinds of data can 
inspire some very interesting applications such as extracting population’s 
mobility pattern from mobile data, detecting anomalies from vehicle GPS 
data, monitoring traffic and quickly responding to events by mining 
historical trajectory data, and analyzing peoples’ spatio-temporal 
behaviors with social and commercial values from their social check-in 
data. Meanwhile, these data are usually noisy, sparse or incomplete, high 
dimensional, and contain both spatial and temporal attributes. Analysis of 
a tremendous amount of such data is a very challenging task.  How to 
clearly summary and explore these complex features in data becomes an 
important problem. It is essential to present the data features in its 
original structure to prevent information loss and facilitate the analysis 
and at the same time provide users with some approaches to characterize 
unique patterns, compare different combinations of features, and quickly 
search anomalies.

Visual analytics solutions show great potential as they can intuitively 
present data’s features including multi-dimensional, spatio-temporal 
attributes, and heterogonous structures and also provide rich 
interactions, allowing users to explore the data and improve mining 
processes and results with their domain knowledge. It is important to keep 
human in the analysis loop to utilize the analyst’s sense of the space and 
time, tacit knowledge of their inherent properties and relationships, and 
space / time -related experiences, which is hard to convey to machines. 
Targeting on the visual summarization and exploration in big data 
especially those containing spatial temporal attributes, in this thesis, 
we propose a set of visual summarization approaches on multi-dimensional 
spatio-temporal data, which cover different aspects of data visual 
analysis issues.
1) We propose a visual analytic approach, which integrated many 
well-established visualization techniques such as parallel coordinates and 
pixel-based representations to characterize data’s mobility-related 
features and summarize user groups inferred from the results.
2) We develop a novel visualization method, Voronoi-diagram-based visual 
design to reveal the unique features related to flow in the data.  This 
visualization method can better reveal the direction information when 
comparing two adjacent flows of time-series data in a graph.
3) We propose a new visual aided mining approach, Visual Fingerprinting 
(VF) for extremely large-scale spatio-temporal feature extraction and 
analysis. The approach integrated important statistical and historical 
information and can be conveniently embedded into urban maps. The 
sophisticated design of the visualization can better reveal frequent or 
periodic patterns for temporal attributes.
4) Finally, we demonstrate the effectiveness and usability of our methods 
by conducting case studies on real datasets including mobile phone data, 
taxi GPS data, and social check-in data. Some interesting findings have 
been obtained.


Date:                   Wednesday, 8 May 2013

Time:                   4:30pm - 6:30pm

Venue:                  Room 5564
                         lifts 27/28

Committee Members:      Prof. Lionel Ni (Supervisor)
                         Dr. Huamin Qu (Supervisor)
 			Dr. Ke Yi (Chairperson)
 			Dr. Lei Chen
 			Dr. Qiong Luo


**** ALL are Welcome ****