SuperMap iServer Streaming Data Technology

11 Mar,2019

The SuperMap streaming data technology solution is based on the Spark Streaming process technology framework of the Spark ecosystem. It is suitable to process streaming data and historical data, and can guarantee the fault tolerance during processing. The SuperMap stream data project will realize the product form and technical support of the entire process of the Internet of Things field from streaming data accessing, streaming data analysis processing, streaming data storage, streaming data outputting and streaming data visualization.

SuperMap iServer Streaming Data Technology Structure Framework

Streaming Data Storage

SuperMap iServer streaming data service can achieve the efficient storage of streaming data based on iServer's Datastore function. It mainly relies on the Elasticsearch distributed streaming database of Datastore to realize real-time searching, stable, reliable and fast functional requirements. Users can store the data that processed by the streaming data or historical data in DataStore, and can fully exploit the value of historical data through the efficient real-time search of the streaming database, such as the historical data track playback and time axis playback.

Streaming Data Output

The SuperMap iServer streaming data service can dynamically track related targets in SuperMap products, and can also alert or notify the target location change behaviors. iServer outputs real-time streaming data through the output connector, including outputting data to iServer DataStore to realize historical data storage, outputting data by message and sending data to clients by WebSoket through the DataFlow Service, which can provide technology basis to streaming data visualization for SuperMap iClient. 

SuperMap Streaming Data Output

Streaming Data Visualization

The SuperMap iServer streaming data service can connect the real-time streaming data to SuperMap products to realize real-time dynamic display. Users can intuitively view the running position status of the current target data at a certain moment.

SuperMap Streaming Data Visualization (Dynamic Monitoring)

Users can also display more real-time target data in dynamic grid aggregation, which is more suitable for massive multi-target data display, and express the flow data status through different color saturations and numbers.

SuperMap Streaming Data Visualization (Data Aggregation)
 

At the same time, the visualization of density distribution and heat distribution can be realized through the spatial aggregation.

SuperMap Streaming Data Visualization (Density Map)

SuperMap Streaming Data Visualization (Trajectory)
 

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