Research on Parallel K-Nearest Neighbor Query Optimization Method for Highway Big Data

Because the information in the field of engineering construction involves remote sensing recognition, intelligent transportation, etc. The rapid growth of various types of information makes highway data continue to develop toward the trend of big data in terms of scale, type, and structure, showing the characteristics of multi-source heterogeneous. This article discusses efficient data retrieval and query methods for specific highway basic databases and simulated highway spatial large data sets. In order to better manage spatial objects and maintain their positional relationship with grid cells, it is proposed to combine inverted index with grid index. In order to break the dependence of the rim radius on the previous traversal results, the serial rim access K nearest neighbor (KNN) algorithm is appropriately improved, and the rim radius is updated by increasing the mesh side length, combined with multi-threading technology Achieve access to multiple rims to improve the query performance of highway big data. Test data by modifying the size, spatial grid sides and these K values, the average performance are improved.