MoveMine
Abstract
With the maturity and wide availability of GPS, wireless, telecommunication, and Web technologies, massive amounts of object movement data have been collected from various moving object targets, such as animals, mobile devices, vehicles, and climate radars. Analyzing such data has deep implications in many applications, such as, ecological study, traffic control, mobile communication management, and climatological forecast. In this article, we focus our study on animal movement data analysis and examine advanced data mining methods for discovery of various animal movement patterns. In particular, we introduce a moving object data mining system, MoveMine, which integrates multiple data mining functions, including sophisticated pattern mining and trajectory analysis. In this system, two interesting moving object pattern mining functions are newly developed: (1) periodic behavior mining and (2) swarm pattern mining . For mining periodic behaviors, a reference location-based method is developed, which first detects the reference locations, discovers the periods in complex movements, and then finds periodic patterns by hierarchical clustering. For mining swarm patterns, an efficient method is developed to uncover flexible moving object clusters by relaxing the popularly-enforced collective movement constraints.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jul 01, 2011
- Source ID
- 10.1145/1989734.1989741
Entities
People
- Bolin Ding
- Jae-Gil Lee
- Jiawei Han
- Lu-an Tang
- Ming Ji
- Roland Kays
- Yintao Yu
- Zhenhui Li
Organizations
- Air Force Office of Scientific Research
- Division of Computing and Communication Foundations
- KAIST
- National Science Foundation
- New York State Museum
- United States Army Research Laboratory
- University of Illinois Urbana–Champaign