数据采集与处理2024，Vol.39Issue(5)：1163-1181,19.DOI:10.16337/j.1004-9037.2024.05.009
面向时空轨迹流的共同运动模式分布式挖掘算法
Distributed Mining Algorithm for Co-movement Patterns in Spatio-Temporal Trajectory Streams
余舒鹏 1吴春雨 1赵斌 1吉根林1
作者信息
- 1. 南京师范大学计算机与电子信息学院/人工智能学院,南京 210023
- 折叠
摘要
Abstract
Mining co-movement patterns from trajectory streams refers to discovering groups of moving objects with same behaviors at the same time,which is essential for transportation logistics,epidemic prevention and control and so on.However,the existing research faces difficulties in responding quickly to large-scale trajectory data streams.Therefore,this paper proposes a novel distributed sliding window algorithm for mining co-movement patterns from spatio-temporal trajectory streams.The algorithm employs a sliding window computing model instead of a snapshot computing model,and utilizes incremental updates instead of re-computing,making it more suitable for handling unbounded and rapidly arriving trajectory data streams.The proposed algorithm demonstrates superior performance in terms of efficiency and effectiveness.Secondly,to address the issue of load imbalance in distributed stream processing systems,this paper proposes an adaptive multi-level dynamic data partitioning strategy.This strategy can adapt to the dynamic changes in trajectory stream data,continuously monitor the system load in real-time,and make appropriate adjustments based on the degree of load imbalance.Finally,this paper implements the above functions on the Flink distributed big data processing platform and uses real data sets for experiments.Comprehensive empirical study demonstrates that the proposed algorithm has faster response speed and lower delay than the baseline method.关键词
时空轨迹流/共同运动模式/分布式系统/滑动窗口/负载均衡Key words
spatio-temporal trajectory streams/co-movement patterns/distributed system/sliding window/load balance分类
信息技术与安全科学引用本文复制引用
余舒鹏,吴春雨,赵斌,吉根林..面向时空轨迹流的共同运动模式分布式挖掘算法[J].数据采集与处理,2024,39(5):1163-1181,19.
