Query Method of Moving Objects in Road Network

Abstract

An embodiment of the present invention provides a method for querying moving objects in a road network, belonging to the fields of high-performance computing and databases. The mobile object query method in the road network is used on the server side, and includes: obtaining mobile object update data and query data input by the user; using a multi-core CPU to update the acquired mobile object update data based on the node where the mobile object is located in the road network Perform clustered indexing and perform clustered indexing on the obtained query data based on the edge where the query data is located in the road network; and put the clustered indexed data into the GPU for calculation based on the nearest neighbor KNN algorithm to obtain query results. Embodiments of the present invention propose a method for querying moving objects in a high-throughput road network based on a new hardware environment, which fully utilizes the characteristics of large memory, multi-core CPU, and GPU, thereby improving the query processing efficiency of moving objects, and more Meet the user query requirements of location-based services under big data.

Description

technical field [0001] The invention relates to the fields of high-performance computing and databases, in particular to a method for querying moving objects in a road network. Background technique [0002] With the widespread popularity of mobile devices and the continuous development of location-based services, query processing for mobile objects has become a research hotspot. Typical queries, such as: find the nearest supermarket or gas station to the user; the nearest taxi to the user, etc. In people's daily life, all objects move based on the road network. For any objects X and Y in the road network, the road network distance d(x,y) between objects can better reflect the actual distance between objects than the Euclidean distance ||x–y||. For example, in a two-way lane, the road network distance d(q,p) from vehicle q to gas station p on the other side is much larger than the Euclidean distance ||q–p|| between them. Therefore, the road network distance d(q,p) between ...

AI Technical Summary

Problems solved by technology

[0003] However, the inventors of the present application found in the process of implementing the present invention that the existing mobile object query algorithm in the road network has the following defects: with the continuous increase of the number of users and the continuous enrichment of application scenarios, the conventional KNN algorithm cannot perform well Respond to emerging applications

For such application scenarios, when the KNN algorithm uses the method of processing one query at a time (one by one), a large number of users need to queue up to wait for the query response, and the waiting time of users in the queue will seriously affect the service quality

[0005] Therefore, the existing mobile object query algorithms in the road network cannot cope with the emerging needs well, and it is necessary to find a new mobile object query scheme to meet the query requirements of location services under big data

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