Real-time query method for graph structure data in rail transit network passenger flow data

Abstract

The invention discloses a real-time query method for graph structure data in rail transit network passenger flow data, which comprises the following steps of: partitioning a data graph with attributesand labels based on hop count, and establishing a relevant index; inputting a query graph, calculating the radius of the query graph and selecting a query starting point; generating a query sequenceof the query graph; filtering out query starting points of the data graph meeting the conditions in each partition according to the characteristics of the query starting points of the query graph; carrying out a cascade graph query process; and collecting query results of all the partitions to complete a distributed graph query process. According to the method, the query graph is transmitted instead of the data graph in the cross-partition query process, so that the data transmission quantity is reduced; the cascading query process is based on the starting point instead of being carried out inthe whole data graph range, so that the query range is greatly reduced; asynchronous concurrency and result set detection during query are adopted, the query efficiency can be maximized, and therefore the real-time query processing requirement for graph structure data in rail transit network passenger flow data can be met.

Description

technical field [0001] The invention belongs to the technical field of data processing, and in particular relates to a real-time query method for graph structure data in rail transit network passenger flow data. Background technique [0002] Rail transit construction is one of the important infrastructure constructions. Currently, the construction of rail transit-related facilities is proceeding rapidly, accompanied by a sharp increase in data size and complexity. For the construction of new rail transit, it is necessary to meet the real-time processing requirements of big data in its life cycle, that is, to meet the requirements of high real-time and low hysteresis when querying. [0003] Network passenger flow data is an extremely important data type in the field of rail transit data. Passenger flow is the most basic and important factor in urban rail transit. The distribution characteristics and changing laws of passenger flow are systematically analyzed to grasp the curr...

AI Technical Summary

Problems solved by technology

Based on the graph query statement decomposition method, the query results of simple sub-query statements need to be combined step by step. For more complex graph query statements with fewer bound nodes, an excessively large intermediate result set will be generated, which will greatly increase the query efficiency. At the same time, this method needs to query in the entire data graph, which also leads to low query efficiency; the iterative query method based on BSP (Block Synchronous Parallel Computing Model) has a high degree of concurrency, but it will generate many unnecessary calculation, because not all nodes need to participate in the iteration in each iteration

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