Abstract: The widespread use of location technology leads to huge volumes of spatio-temporal data collected in the form of tra-jectory data streams.How to discover useful information from it has attracted more and more scholars’ attention.Mining companion pattern from trajectory stream refers to discovering groups with highly similar behaviors at the same time,which is essential for real-time applications of traffic management and recommendation systems.However,the existing research only achieves a second-level response,and it is difficult to respond quickly in milliseconds to large-scale trajectory data.Therefore,this paper proposes a distributed companion patterns mining framework DCPFS.The main contents of our work include:1) In order to reduce the time consumption of the density-based clustering algorithm DBSCAN for large-scale data,this paper proposes a data partition strategy and clustering merging algorithm based on a distributed deployment plan to ensure clustering parallelism and accuracy.2) Because the trajectory movement is directional in reality,we increase the direction dimension to reduce the redundancy in the clustering.3) We designed a parallel intersection algorithm to improve the efficiency of the intersection of clustering results in the pattern mining stage.4) We implement DCPFS on the Flink distributed big data processing platform and use Chengdu taxi GPS dataset and Google life dataset for experiments.Comprehensive empirical study demonstrates that the proposed framework has faster response speed than the baseline method.

Key words: Big data, Trajectory streams, Companion patterns, Density-based clustering, Distributed