In recent years, with the popularization of the mobile devices, more and more location-based applications have been developed. As a result, location data of various objects is widely available. Identifying object groups that tend to move together is an emerging research topic. Existing approaches for identifying mobile group patterns assume the existence of raw location data which records a given object¡¦s position at every equal-spaced time point. However, a moving object may become disconnected voluntarily or involuntarily from time to time, and thus this assumption may not always valid. In this research, we describe the locations of moving object as a (non-continuous) trajectory function. Based on the new model, we re-define the mobile group mining problem and develop efficient algorithms for mining mobile groups. The proposed algorithms are evaluated via synthetic data generated by IBM City Simulator.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0730104-102312 |
| Date | 30 July 2004 |
| Creators | Liu, Ying-Han |
| Contributors | Chih-Ping Wei, San-Yi Huang, Fu-Ren Lin |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0730104-102312 |
| Rights | restricted, Copyright information available at source archive |
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