SuperMap iServer Loads 9D Trillions of Flight Trajectories

03 Nov,2017

Nowadays, big data brings convenience for our lives, like the real-time road statues in the navigation apps, real-time vehicle locations in car calling apps. These services is composed of real-time location acquisition, transmission, calculation, demonstration, etc. In SuperMap GIS 9D, we also provide the related solutions from real-time data processing to massive data statistics and analysis.

SuperMap iServer 9D real-time data service uses Spark Streaming calculation framework. It has the distribution processing of Spark Streaming as well as the processing algorithms based on iObjects for Spark.

Real-time data processing procedure

Real-time data processing usually has the following 4 steps:

Receivers: receive or acquire data through Socket, HTTP, JMS and specified catalog files and analyzed internal object model.

Filter: In order to ensure the accuracy of the data, we need to filter the data first.

Mapper: multiple feature attribute data operations will be added in this step and different new attributes can be processed in accordance with the latest status.

Sender: send the processed data as notification or store permanently so that the errors can be dealt immediately.


Real-time data publishing services

After knowing the internal process of real-time data processing, we can take a look at the overall process of real-time data processing servicds, we can display the data using iClient or iObjects. 

Then, using airport as an example to write the model files and then publish using iServer products.

Write client program to visualize the results.

Performance comparison

We use 3,455 airports around the world to test the performance and got the following results. (Unit: event/second) 

SourceSpeed ( e/s )

From the performance testing, SuperMap iServer 9D performs very smoothly and is extensible.

Based on Spark Streaming calculation architecture, using model file to define calculation process, making SuperMap iServer 9D with high performance and code-free.

In addition, the data acquired by real-time data services can be analyzed by iObjects for Spark, like trajectory reconstruction, density analysis, grid clustering, attribute summary, element connection, etc.


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Tags: iServer

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