Mining Behavioral Patterns From Mobile Big Data

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis Defence


Title: "Mining Behavioral Patterns From Mobile Big Data"

By

Mr. Tong LI


Abstract

The widespread usage of Internet-connected mobile devices allows users' 
behavior to be recorded in large-scale and fine-grained digital traces in 
cyberspace. Such data, termed mobile big data, contain great social, 
economic, and academic values. Analyzing mobile big data has significant 
implications for all relevant stakeholders, ranging from smartphone 
manufacturers, network operators to app developers.

This thesis aims to discover and understand behavioral patterns from 
mobile big data based on large-scale and real-world datasets. 
Specifically, this thesis reveals behavioral patterns from three scales, 
i.e., short-term patterns, long-term patterns, and disrupted patterns. 
Firstly, we explore short-term temporal patterns and propose a framework 
to discover users' daily activity patterns from their mobile app usage. By 
applying the framework to a real-world dataset consisting of 653,092 
users, we successfully extract five common patterns among millions of 
people, including commuting, pervasive socializing, nightly entertainment, 
afternoon reading, and nightly socializing. Secondly, we leverage 
short-term spatiotemporal app usage patterns to reveal urban dynamics. We 
prove the strong correlation between mobile usage behavior and location 
features, which brings a new angle to urban analytics. Thirdly, we mine 
the behavior patterns from the long-term scale. We reveal the longitudinal 
evolution of mobile app usage by conducting a study on 1,465 users from 
2012 to 2017. The results show that users' app usage evolves over time. 
However, the evolutionary processes in app-category usage and individual 
app usage are different in terms of popularity distribution, usage 
diversity, and correlations. Lastly, we study how the behavioral patterns 
were disrupted through extreme global events, i.e., the pandemic of 
Covid-19. We collect mobile usage records of 452 users in North America. 
We then manifest the potential for inferring Covid-19 outbreak stages by 
leveraging disrupted mobile usage patterns. In the end, we conclude this 
thesis with future research directions and challenges related to mobile 
big data analysis.


Date:			Thursday, 22 July 2021

Time:			10:00am - 12:00noon

Zoom Meeting: 
https://hkust.zoom.us/j/97986975852?pwd=Y2N2YjVNNzhqZkRYdEVYVElSWGZ5Zz09

Chairperson:		Prof. Beifang CHEN (MATH)

Committee Members:	Prof. Pan HUI (Supervisor)
 			Prof. James KWOK
 			Prof. Raymond WONG
 			Prof. Danny TSANG (ECE)
 			Prof. Meeyoung CHA (KAIST)


**** ALL are Welcome ****