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Mining Vehicles Frequently Appearing Together from Massive Passing Records |
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PP: 1427-1433 |
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Author(s) |
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Dongjin Yu,
Wensheng Dou,
Wanqing Li,
Suhang Zheng,
Jianhua Shao,
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Abstract |
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| Vehicles Frequently Appearing Together, or VFATs, can be clues in solving criminal cases. Traditional sequence mining
approaches help identify VFATs from passing-through records collected at monitoring sites. However, huge traffic data streams hinder
fast identification of VFATs. In this paper, we present a multi-threaded approach to fast identification of VFATs based on multi-core
processors, called Frequent Sequential Mining based on Multi-Cores (FSMMC). It parallels the execution of tasks, partitions large
volumes of data, and obtains VFATs by merging local candidates discovered in different threads running on different processor cores.
Through local parallel reduction, FSMMC eliminates the repetitive patterns and reduces computational effort. Moreover, it achieves
workload balance by the dynamic distribution of tasks to a pool of threads where the thread that finishes first joins another running
thread. Both theoretical analysis and case studies show that FSMMC takes full advantage of multi-core computing platforms and
has higher speed-up when searching VFATs among massive passing through records, compared with other approaches without multithreading. |
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