Track flow data-oriented continuous range query method and system

Abstract

The invention provides a track flow data-oriented continuous range query method and system. The system comprises a position information sending module, an index module and a query module. According to the invention, an index module is improved firstly, it can be guaranteed that track points are rapidly inserted according to the spatial positions and the time sequence of the track points, and ID-time range query and spatial range query of tracks are supported. Thirdly, a query module is improved, an internal and external memory combined query mechanism is adopted, and the number of times of spatial intersection judgment and the number of times of data needing to be retrieved are reduced to the maximum extent; and furthermore, an index module and a query module are supplemented, so that the query accuracy can be ensured under the condition that the query is changed. Finally, a memory cleaning module is further included, when the number of query times and the number of concurrent queries are increased, a result set is reduced, and the memory burden is relieved. According to the invention, under the condition of ensuring real-time updating of data, time range query and space range query are realized at the same time, and the efficiency of continuous range query is improved.

Description

technical field [0001] The present invention relates to a continuous range query technology, in particular to a continuous range query technology for trajectory stream data Background technique [0002] With the development of the global positioning system and the popularization of mobile devices, moving objects are continuously generating a large amount of streaming data at high speed. The processing of convective data has become an emerging technology hotspot and has attracted widespread attention. [0003] Currently, there have been some studies on querying streaming data. [0004] Real-time query for microblogging data and moving objects is one of them. The index structure for this query is a grid or pyramid structure. Since the targeted data is of point type, these two structures involve the merging and splitting of nodes when inserting and deleting data, and the entire structure may need to be adjusted, so it is difficult to update, and the efficiency of point-by-po...

AI Technical Summary

Problems solved by technology

The method proposed by WaterWheel cannot efficiently support spatial range queries, and can only handle queries based on ID and time attributes

Dragoon only solves the problem of real-time data update and ordinary single query problem, and still adopts the method of batch update index for real-time data update, which cannot guarantee that the latest data will appear in the query result set in a timely manner. In addition, in continuous The efficiency of the query is still not improved

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