摘要
The clustering of trajectories over huge volumes of streaming data has been rec- ognized as critical for many modem applica- tions. In this work, we propose a continuous clustering of trajectories of moving objects over high speed data streams, which updates online trajectory clusters on basis of incremental line- segment clustering. The proposed clustering algorithm obtains trajectory clusters efficiently and stores all closed trajectory clusters in a bi- tree index with efficient search capability. Next, we present two query processing methods by utilising three proposed pruning strategies to fast handle two continuous spatio-temporal queries, threshold-based trajectory clustering queries and threshold-based trajectory outlier detections. Finally, the comprehensive experi- mental studies demonstrate that our algorithm achieves excellent effectiveness and high effi- ciency for continuous clustering on both syn- thetic and real streaming data, and the propo- sed query processing methods utilise average 90% less time than the naive query methods.
The clustering of trajectories over huge volumes of streaming data has been recognized as critical for many modern applications.In this work,we propose a continuous clustering of trajectories of moving objects over high speed data streams,which updates online trajectory clusters on basis of incremental linesegment clustering.The proposed clustering algorithm obtains trajectory clusters efficiently and stores all closed trajectory clusters in a bitree index with efficient search capability.Next,we present two query processing methods by utilising three proposed pruning strategies to fast handle two continuous spatio-temporal queries,threshold-based trajectory clustering queries and threshold-based trajectory outlier detections.Finally,the comprehensive experimental studies demonstrate that our algorithm achieves excellent effectiveness and high efficiency for continuous clustering on both synthetic and real streaming data,and the proposed query processing methods utilise average90%less time than the naive query methods.
基金
supported by the National Natural Science Foundation of China under Grants No.61172049,No.61003251
the National High Technology Research and Development Program of China(863 Program)under Grant No.2011AA040101
the Doctoral Fund of Ministry of Education of Chinaunder Grant No.20100006110015
