Similarity join is a fundamental operation for managing semantic trajectory data. In massive data scenarios, distributed paradigms can be utilized to process similarity joins for huge amounts of semantic trajectory data, but they face the challenge of locally aware partitioning of the data. To address this problem, we propose a distributed similarity join framework based on trajectory segments. The semantic trajectory data are partitioned by segments, and semantically similar trajectories are stored in the same partition to improve the local similarity of the partitions. We design global and local indexes to efficiently manage partitioned data. We develop a filtering validation framework to enhance similarity query performance by pruning irrelevant trajectories based on the temporal, spatial, and semantic distances of trajectory segments. Extensive experiments on three real-world datasets demonstrate that our method achieves superior scalability and query efficiency, compared to other methods.

With the rise of mobile terminals and the maturity of positioning technology, the amount of available spatiotemporal data continues to grow rapidly, so it is crucial to be able to process it efficiently. This paper proposes a multi-level indexing technique based on context dimension awareness. It first selects the partition order by the unit scale of each context dimension in the dataset. Second, it consider the distribution of each context dimension in the dataset, choose an appropriate partitioning method, and divide the dataset into multiple balanced splits. We test the method on real-world datasets, and experiments show that the speed of query execution increased and resource-use efficiency improved by our approach.