A real-time trajectory indexing method based on MongoDB

Aiming at the inefficiency problems of existing trajectory database techniques especially in real-time access to latest trajectories, a real-time trajectory indexing method based on MongoDB and mixed with spatio-temporal R-tree, hash table and B-tree for searching leaf nodes is proposed in this paper. Time in spatio-temporal R-tree is used as another dimension of equal status to space, and a leaf node can only involve a moving object´s consecutive trajectory points. In order to solve the problem of frequent updates and lack of memory, hash table is divided into two kinds: one caches leaf nodes of spatio-temporal R-tree, which are not inserted into spatio-temporal R-tree until they are full or out-dated in the hash table. This improves generation efficiency of real-time trajectory index; the other one caches in-memory nodes which are loaded from external memory, it avoids frequent operations related to external memory. We build B-tree based on object identification and time in leaf nodes, which benefits trajectory queries for moving objects. In comparison to SETI, the experimental results show that our method has good update efficiency and query performance, and it meets the demand of common trajectory queries in present applications.