Quality-aware trajectory processing using significant locations

TL;DRAbstract

Driven by major advances in sensor technology, GPS-enabled mobile devices and wireless communications, a large amount of trajectory data is currently generated and managed in scores of application domains. This inspires a tremendous amount of research effort analyzing large scale trajectory data from a variety of perspectives in the last decade. However, people are still witnessing that data quality issues still persist in trajectory data and various kinds of trajectory-based services, mainly at 3 different levels: (1) the data level, e.g., heterogeneous and uncertain trajectory data; (2) the service level, e.g., the inability of capturing latent factors behind trajectory data; and (3) the representation level, e.g., the lack of semantic meaning in existing representation techniques of trajectory data. Such quality issues can be tied to the process and limited techniques that generate trajectory data, and the way that trajectory data is stored and presented. In this thesis , we tackle

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