With the development of 5G communications, location-based services (LBS) mobile applications have already become ubiquitous. As a result, applications like foursquare and Twitter have generated a massive amount of semantic trajectory data, which can no longer be efficiently processed by a single machine due to computing power limitations. Among the processing and analysis of semantic trajectory data, the similarity join query is a general yet computationally complex query operation. It is widely used in different applications, such as route planning, personalized carpooling, and geographic location recommendations. This paper proposes a distributed join framework to process similarity join over large sets of semantic trajectories. The framework supports the similarity join of semantic trajectories in the textual, temporal, and spatial domains. Experimental results on real datasets show that in comparison with baselines, the distributed framework in this paper has excellent scalability and query efficiency.

The rapid growth of large-scale trajectory data has prompted researchers to develop multiple large-scale trajectory data management systems. One of the fundamental requirements of all these systems, regardless of their architecture, is to partition data efficiently between machines. In the typical query operations of tracks, the query on ID is a frequent operation of track query, such as ID time range query, ID space range query, etc. A widely used ID indexing technique is to reuse an existing search tree, such as a Kd-tree, by building a temporary tree for the input samples and using its leaf nodes as partition boundaries. However, we show in this paper that this approach has significant limitations. To overcome these limitations, we propose a new index, BOD-tree, which inherits the main features of the Kd-tree and can also partition the dataset into multiple balanced splits. We test the method on real datasets, and extensive experiments show that our algorithm can improve resource usage efficiency. \end{abstract}

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.