Efficient Dimensionality Reduction and Query Algorithm of Trajectory Data Based on Spatial Position Relation

Due to the rapid development of information technology, society is in a critical period of digitalization and information transformation, and the demand for information systems based on database technology in various industries is becoming increasingly prominent. Location-based services rely on massive real-time generated trajectory data. In the processing of hundreds of millions of continuously changing trajectory data, dimensionality reduction algorithm and query technology have been the key to research. By reducing the scale of trajectory data and reducing the time of data processing during query operations, the performance of query can be effectively improved, and whether high-quality and efficient query can be achieved is very important for the database. In this paper, a UGC(uniform grid code) and a NGDR(non-uniform grid dimensionality reduction algorithm) for trajectory data are proposed, which convert the coordinates of trajectory data into one-dimensional string storage, merge the grids that do not meet the requirements, fully retain the complex interrelationship between trajectory data through spatial position mapping, and use range query and nearest neighbor query to test the performance of the reduced data. The real trajectory and virtual generated trajectory data in different cities are used as datasets, and the uniform grid code algorithm, non-uniform grid algorithm proposed in this paper are compared with three benchmark methods. Experiments show that the spatial position relationship similarity of the data after NGDR can be up to 82.5%. The range query time of NGDR is improved at least by 73.86% compared with the other queries, and the nearest neighbour query time is improved at least by 52.26%, which achieves better results than other benchmark methods.