Upgrade the Four Major Capabilities of SuperMap iServer in Processing Automation Services

Processing automation (GPA) tool is designed to address issues such as high application barriers and low analysis efficiency in traditional spatiotemporal big data processing. In the SuperMap iServer GPA service, a concise modeling page is provided, which enables the automation of data processing and analysis processes without the need for coding. Building a business process model through simple drag and drop methods significantly improves work efficiency and significantly reduces development costs. To further meet user needs, SuperMap has upgraded its various capabilities for handling automation services in SuperMap iServer 2023, providing more convenient automation execution, safer model sharing, more comprehensive analysis tools, and more efficient computing performance.

01 GPA Tool Assists Real Time Data Processing

Meteorological decision-making services have high timeliness requirements for the processing of meteorological data. In order to achieve real-time automation of meteorological observation data and meteorological prediction data, and regularly output standardized result data. GPA services with new planned task functions, enabling automated execution of business models without the need for manual intervention. The planning task function provides multiple types of triggers to meet different business needs. The default trigger supports timed trigger execution models, as well as monitoring file changes to trigger execution models. At the same time, tool parameters in the model can also be bound in the trigger, and the changed files can be passed to the model for execution.

Taking the hourly updated national wind direction data as an example, the meteorological information center business platform writes the updated national wind direction data into the PostGIS database every hour, and needs to generate point single value thematic maps and publish map services. By GPA service modeling pages and building models for automatic mapping and service publishing, combined with timed task functions, the entire process from data to services can be automated, saving manpower investment and achieving cost reduction and efficiency increase.

02 Model Encryption Protection Ensures Secure Sharing

The completed processing automation model has the ability to be easily reused, supporting one click export and sharing with others. In order to ensure that business processes are not leaked, a new model library has been added to manage the models through processing automation services. The model library provides multiple encryption protection capabilities and supports setting passwords and permissions. The model library supports three types of permissions: executable, viewable, and modifiable, and these permissions can be flexibly combined as needed to ensure more secure and controllable model sharing.

03 Rich Tools to Assist Intelligent Decision-making

The multi-source and multidimensional spatiotemporal data contains rich information, but it also increases the complexity of data analysis. In order to meet diverse analysis needs, support intelligent applications in different fields, and provide more comprehensive analysis tools for GPA services. In the 2023 version, a total of 140 new tools have been added, covering areas such as image processing, 3D data, knowledge graph, and spatial analysis, with a total of over 1000 predefined tools.

In addition, in order to meet the customized needs of the business, GPA tool provides flexible and scalable capabilities. Technicians can develop custom tools using programming languages such as Java/Scala/Python, or fully utilize existing heterogeneous tools and services to register command lines and HTTP service interfaces as tools. Extended custom tools can participate in modeling and schedule execution through GPA tool. By combining custom and predefined tools, we can fully utilize existing achievements and enhance our comprehensive analysis and decision-making abilities.

04 High Performance Support for Massive Data Computing

In order to meet the growing demand for big data analysis, GPA services have further improved the computational performance of common standalone analysis tools and upgraded the Spark distributed computing framework.

By optimizing algorithms, fully utilizing spatial indexing and parallel computing capabilities, the computational performance of single machine analysis tools such as vector resampling, grid re grading, and grid to vector surface has been improved. Significantly improve data processing efficiency while shortening project deployment time.

Improvement factor=(time spent before improvement - time spent after improvement)/time spent after improvement

▲ High performance support for massive data computing

The upgrade of the Spark distributed computing framework is facing the increasingly large volume of multi-source multidimensional spatiotemporal data, and traditional single machine computing methods cannot meet the real-time processing needs of massive data. Therefore, processing automation technology fully utilizes the advantages of distributed technology and provides over 250 big data tools. These big data tools are designed based on a distributed architecture and can interface with distributed storage databases and distributed computing clusters. The 2023 version upgrades the Spark distributed computing framework from 2.4.3 to 3.3.0, achieving efficient processing and computation of massive data.

In summary, the SuperMap iServer 2023 GPA service has been upgraded and strengthened with four major capabilities, making complex data processing and analysis simple and efficient. At present, GPA services have been widely applied in various fields such as natural resources, land planning, meteorology and water conservancy.

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