Mining periodicity and object relationship in spatial and temporal data

Data mining;spatial and temporal data;movements;periodicity;object relationship;MoveMine

Li, Zhenhui

关键词: Data mining;    spatial and temporal data;    movements;    periodicity;    object relationship;    MoveMine;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/42370/Zhenhui_Li.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

With the rapid development of positioning technologies, sensor networks, and online social media, spatiotemporal data is now widely collected from smartphones carried by people, sensor tags attached to animals, GPS tracking systems on cars and airplanes, RFID tags on merchandise, and location-based services offered by social media. While such tracking systems act as real-time monitoring platforms, analyzing spatiotemporal data generated from these systems frames many research problems and high-impact applications.During my PhD study, I have extensively studied data mining algorithms for moving objects.I have contributed several key algorithms to this exciting field. I have proposed the very first work to detect periodicity from movement data even if the movement only has rough periodicity and has lots of non-periodic random short-trajectories. I have also systematically studied a broad range of relationship patterns among moving objects in practical scenarios. Objects forming a social cluster, for example, can be efficiently extracted from large-size moving object pool even if the objects in a group only have sporadic interactions. I have further conducted an examination on when, where and how moving objects interact in a sporadic way, in order to discover semantic relationships, such as friends, colleagues and family. The algorithms have been integrated into our MoveMinesystem, an online system allowing people to test our data mining methods on a diverse collection of real movement datasets.For future work, my long-term research plan is to study cyber-physical systems, such as ecological systems, patient-care systems, and transportation systems. Such systems consist of a large number of interacting spatial, temporal and information components.