Mining moving objects trajectories in Location-based services for spatio-temporal database update
Advances in wireless transmission and mobile technology applied to LBS (Location-based Services) flood us with amounts of moving objects data.Vast amounts of gathered data from position sensors of mobile phones,PDAs,or vehicles hide interesting and valuable knowledge and describe the behavior of moving objects.The correlation between temporal moving patterns of moving objects and geo-feature spatio-temporal attribute was ignored,and the value of spatio-temporal trajectory data was not fully exploited too.Urban expanding or frequent town plan change bring about a large amount of outdated or imprecise data in spatial database of LBS,and they cannot be updated timely and efficiently by manual processing.In this paper we introduce a data mining approach to movement pattern extraction of moving objects,build a model to describe the relationship between movement patterns of LBS mobile objects and their environment,and put up with a spatio-temporal database update strategy in LBS database based on trajectories spatiotemporal mining.Experimental evaluation reveals excellent performance of the proposed model and strategy.Our original contribution include formulation of model of interaction between trajectory and its environment,design of spatio-temporal database update strategy based on moving objects data mining,and the experimental application of spatio-temporal database update by mining moving objects trajectories.
LBS (Location-based Services) spatio-temporal data mining mining moving trajectory objects spatiotemporal database update strategy
Danhuai Guo Weihong Cui
Institute of Remote Sensing Applications,Chinese Academy of Sciences,Beijing 100101
国际会议
第16届国际地理信息科学与技术大会(16th International Conference on GeoInformatics and the Joint Conference)
广州
英文
2008-06-28（万方平台首次上网日期，不代表论文的发表时间）