中文摘要：

基于对车牌识别大数据的处理与分析，可以完成伴随车辆组的发现，在涉案车辆追踪等方面具有广泛的应用。然而当前单一机器模式下伴随车辆组发现算法存在时间和空间上处理性能低下等问题。针对此问题，提出了一种伴随车辆组发现方法——FP-DTC方法。该方法将传统的FP-Growth算法利用分布式处理框架Spark进行了并行化，并作了相应的改进和优化来更加高效地发现伴随车辆组。实验结果的分析表明，提出的方法能够很好地解决车牌识别大数据上的伴随车辆组发现问题，性能相比采用同样方法的Hadoop实现提升了近4倍。

英文摘要：

The discovery of travelling companions based on processing and analysis of the license plate recognition big data has become widely used in many aspects such as the involved vehicle tracking. However, discovery algorithms of travelling companions have poor performance in single machine mode no matter in time and space. To solve this problem, a discovery method of travelling companions named FP-DTC was proposed. This method based on the algorithm of FP-Growth was parallelled by the distributed processing framework-Spark, and had made some improvement and optimization to discover the travelling companions more efficiently. The experimental results show that, this method performs well on the discovery of travelling companions, and achieves an increase of nearly four times than the same algorithm with Hadoop.