k-Nearest Neighbor Trajectory (k-NNT) Query is a basic and important spatial query operation widely used in many fields, such as intelligent transportation and urban planning. However, with the rapid increase of trajectory data volume, traditional k-NNT query algorithms for centralized environment are not effective and scalable enough, because the computational complexity increases dramatically when the spatial continuity of trajectories is considered. To address this problem, we propose a

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... ibuted grid index for trajectory data which partitions the trajectory into grids under MapReduce framework. Furthermore, a parallel query approach MR-GB-KNNT is proposed based on the proposed grid index to improve the efficiency and scalability of the k-NNT query. The experiment demonstrates that MR-GB-KNNT could perform well in cloud computing environment and improve the querying performance of the k-NNT. call for the cloud computing platform to provide a promising method to handle the trajectory of large volume and complex format. Therefore, in order to support the efficient k-NNT query algorithm in distributed environment, we provide a grid index for trajectory data and an auxiliary structure of the trajectory rebuilding table with considering of the spatial and temporal characteristics of location and trajectory data. Finally, we implement the distributed k-NNT query based on MapReduce. The rest of this paper is organized as follows. Section 2 reviews the related work. Section 3 provides the preliminaries. In Section 4, we present the processing framework and the detailed solution of the k-NNT query in the MapReduce model. Section 5 evaluates the experimental results. We conclude in Section 6. 8 Copyright ⓒ 2017 SERSC raises, the number of candidate trajectories becomes less than k, it costs more time on executing more CircularTrip(). But for MR-Base-KNNT, the query time is always growing slowly.