Mining Heterogeneous Data for Semantic Understanding of Mobility Data
Open Access
- Author:
- Wu, Fei
- Graduate Program:
- Information Sciences and Technology
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- February 26, 2018
- Committee Members:
- Zhenhui Li, Dissertation Advisor/Co-Advisor
Zhenhui Li, Committee Chair/Co-Chair
Clyde Lee Giles, Committee Member
Xiang Zhang, Committee Member
Wang-Chien Lee, Outside Member - Keywords:
- semantic annotation
mobility data
data mining - Abstract:
- With the prevalence of positioning technology, an increasing amount of human mobility data becomes available nowadays, including geotagged social media data, location records collected by mobile phone applications, and GPS traces collected by navigation services. There have been tremendous interests in mining different mobility patterns for understanding human activities and behaviors. While the mobility data are valuable, they are often numeric or categorical in nature, (e.g., a GPS point with timestamp and geo-coordinates). As a result, the subsequent mined patterns from the data often have limited semantics. At the same time, a massive volume of spatial contexts (e.g., venue databases and geotagged tweets) provides us with rich semantics about urban dynamics. By combining the mobility data with surrounding contexts, we are able to understand the semantics of human mobility. Semantics enriched mobility data can benefit various applications such as automatic human activity space inference and target advertisements. This dissertation describes several recent attempts in fusing external context data for understanding the human mobility data. I will motivate the problem by presenting one key limitation of conventional mobility pattern mining approaches. A fundamental step in understanding the semantics is the mobility record annotation problem, that is, to associate a raw mobility record with the relevant surrounding context. More specifically, I have proposed to use words from social media as a source of dynamic event information. I have also proposed a framework to model dependencies among annotations for individual and collective movements. As the context data could have duplicates, I have proposed a label propagation method for addressing the annotation problem under noisy contexts. Finally, I will present recent collaboration results on applying the proposed framework to a practical social science study.