M.Sc. Tong Li defends his doctoral thesis Mining Behavioral Patterns from Mobile Big Data on Friday the 20th of May 2022 at 12 o'clock in the University of Helsinki Physicum building, Room E204 (Gustaf Hällströmin katu 2, 2nd floor). His opponent is Associate Professor Konstantinos Stefanidis (Tampere University, Finland) and custos Professor Sasu Tarkoma (University of Helsinki). The defence will be held in English. It is possible to follow the defence as a live stream at https://helsinki.zoom.us/j/66406416222.
The thesis of Tong Li is a part of research done in the Department of Computer Science and in the Content-Centric Structures and Networking as well as Systems and Media groups at the University of Helsinki. His supervisors have been Professors Pan Hui and Sasu Tarkoma (University of Helsinki).
Mining Behavioral Patterns from Mobile Big Data
Mobile devices connected to the Internet are a ubiquitous platform that can easily record a large amount of data describing human behavior. Specifically, the data collected from mobile devices --- referred to as mobile big data reveal important social and economic information. Therefore, analyzing mobile big data is valuable for several stakeholders, ranging from smartphone manufacturers to network operators and app developers.
This thesis aims to discover and understand behavioral patterns from mobile big data based on large real-world datasets. Specifically, this thesis reveals patterns from three domains: people, time, and location. First, we explore mobile big data from the people domain 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. Second, still from the people domain, we derive group health conditions by using their smartphone usage data. In particular, we collect mobile usage records of 452 users in North America. We then demonstrate the potential for inferring group health conditions (i.e., COVID-19 outbreak stages) by leveraging less privacy-sensitive smartphone data, including CPU usage, memory usage, and network connections. Third, we mine the behavior patterns from the time domain. We reveal the evolution of mobile app usage by conducting a longitudinal study on 1,465 users from 2012 to 2017. The results show that users' app usage significantly changes over time. However, the evolution in app-category usage and individual app usage are different in terms of popularity distribution, usage diversity, and correlations. Last, with respect to the location domain, we leverage city-scale spatiotemporal mobile app usage data to reveal urban land usage patterns. We prove the strong correlation between mobile usage behavior and location features, which brings a new angle to urban analytics
Availability of the dissertation
An electronic version of the doctoral dissertation is available on the e-thesis site of the University of Helsinki at http://urn.fi/URN:ISBN:978-951-51-8173-2.
Printed copies will be available on request from Tong Li: email@example.com.