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ScaleOut Software Releases Open-Source APIs for Digital Twins and Development Workbench

ScaleOut Software Releases Open-Source APIs for Digital Twins and Development Workbench

Digital twin APIs and new development workbench enable users to easily build applications for real-time monitoring and simulation

ScaleOut Software announces the first open-source release of its digital twin APIs and development workbench for its ScaleOut Digital Twins hosting platform. Digital twin APIs allow developers to build powerful applications for real-time monitoring and simulating large systems. The workbench enables fast development and testing of digital twin applications before deployment. To help accelerate the development of digital twin applications, ScaleOut Software is releasing these components as open source under the Apache 2.0 license.

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“We are excited to open source our digital twin APIs and new development workbench so that application developers can easily use digital twins to create a new generation of applications”

Going beyond product lifecycle management (PLM), which focuses on improving product design, digital twins enable important new capabilities for real-time monitoring and simulating systems with thousands of components. For example, they allow applications to analyze streaming data in real time instead of requiring offline batch analytics. They can also simulate large complex systems to improve design choices and decision making. Digital twins offer key benefits to data analysts and managers in a wide range of industries, including transportation, logistics, security, healthcare, disaster recovery, and financial services.

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“We are excited to open source our digital twin APIs and new development workbench so that application developers can easily use digital twins to create a new generation of applications,” said Dr. William Bain, ScaleOut Software’s CEO and founder. “These freely accessible software components should simplify and accelerate the development of digital twin applications and encourage community participation.”

The ScaleOut Digital Twins™ platform uses highly scalable, in-memory computing technology to host thousands of digital twins for monitoring real-time data from IoT devices and other data sources, enabling real-time analytics that provides actionable results in seconds. The platform also runs large-scale simulations that aid in the design of complex systems, such as airline logistics and traffic control networks.

Application developers use ScaleOut’s open-source APIs to build digital twin models for deployment on the ScaleOut Digital Twins platform. They can now use the open-source workbench to test application code built using these APIs and gain immediate feedback that shortens the design cycle. Once tested, developers can deploy applications on the platform to run at scale with thousands of digital twins.

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Key Features and Benefits for ScaleOut Software’s Digital Twin APIs and Workbench:

  • Easily Build Digital Twin Models: ScaleOut’s open-source APIs enable developers to build digital twin models in Java or C#. These models serve as templates for creating digital twins. The APIs employ standard, object-oriented design principles to simplify development and avoid the use of specialized or platform-specific techniques.
  • Easily Test Digital Twin Models: ScaleOut’s open-source workbench lets developers test their digital twin models for both real-time monitoring and simulation in the user’s native development and test environment. The workbench accelerates application development by avoiding the need to deploy models to the hosting platform during the development process.
  • Greater Ease of Access and Flexibility: Because ScaleOut’s digital twin APIs and workbench are freely available as open-source components, developers can download them at n****** and immediately start building digital twin applications. These APIs can also serve as the basis for creating digital twins that run on other platforms and provide cross-platform compatibility.
  • Enable High Performance: ScaleOut’s digital twin model simplifies hosting on scalable, in-memory computing platforms. In-memory hosting dramatically reduces latency when digital twins access state information or process messages for both real-time analytics and simulation. It also enables high processing rates that support the creation of thousands of digital twins.

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