Date of Award
5-10-2017
Degree Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Computer Science
First Advisor
Rafal Angryk
Second Advisor
Petrus Martens
Third Advisor
Rajshekhar Sunderraman
Fourth Advisor
Zhipeng Cai
Abstract
Finding frequent patterns plays a vital role in many analytics tasks such as finding itemsets, associations, correlations, and sequences. In recent decades, spatiotemporal frequent pattern mining has emerged with the main goal focused on developing data-driven analysis frameworks for understanding underlying spatial and temporal characteristics in massive datasets. In this thesis, we will focus on discovering spatiotemporal event sequences from large-scale region trajectory datasetes with event annotations. Spatiotemporal event sequences are the series of event types whose trajectory-based instances follow each other in spatiotemporal context. We introduce new data models for storing and processing evolving region trajectories, provide a novel framework for modeling spatiotemporal follow relationships, and present novel spatiotemporal event sequence mining algorithms.
DOI
https://doi.org/10.57709/10062766
Recommended Citation
Aydin, Berkay, "Discovery of Spatiotemporal Event Sequences." Dissertation, Georgia State University, 2017.
doi: https://doi.org/10.57709/10062766