This research project is to investigate principles and methods for uncovering sophisticated patterns and actionable knowledge from massive moving object data. Thanks to the rapid progress and broad adoption of sensor, GPS, wireless network, and other advanced technologies, moving object data have been accumulating in unprecedented scale. However, moving object data could be dynamic, sparse, scattered, and noisy, and patterns and knowledge to be mined could be deeply hidden, sophisticated, and subtle. The MoveMine project investigates effective and scalable methods for mining various kinds of complex patterns from dynamic and noisy moving object data, finding multiple interleaved periodic patterns, and performing in-depth multidimensional analysis of moving object data. It integrates and extends multiple disciplinary approaches derived from spatiotemporal data analysis, data mining, pattern recognition, statistics, and machine learning. The study takes bird and animal movement data and traffic data as the major sources of data for investigation. However, developed methods can be applied to the analysis of many other kinds of moving object data for environmental study, traffic control, law enforcement, and protection of homeland security. The study also addresses the issue of ensuring privacy and security protection while developing powerful pattern and knowledge discovery mechanisms. The research results are to be published in various research and application forums and be integrated into the educational programs at UIUC. The progress of the project and the research results are also disseminated via the project Web site (www.cs.uiuc.edu/homes/hanj/projs/movemine.htm).
