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ScaleOut Software Adds Simulation Capabilities to Its Digital Twin Streaming Service

CIO Review

New features enable users to simultaneously model and simulate thousands of interacting data sources and improve decision making prior to deployment

BELLEVUE, Wash - ScaleOut Software announces new device modeling and simulation capabilities for its ScaleOut Digital Twin Streaming Service™. These capabilities extend the service’s ability to perform real-time analytics at scale using digital twins to track devices and other real-world data sources. Users can now simulate the behavior of these data sources using digital twins prior to deployment as a tool to evaluate design choices and improve decision making. This offers several benefits to data analysts and managers in a wide range of industries, including telematics, logistics, security, healthcare, crime prevention, and financial services.

“We are excited to extend the capabilities of our streaming service to harness digital twins for both simulation and real-time analytics”

Going beyond product lifecycle management (PLM), which focuses on improving product design, ScaleOut Software’s new simulation capabilities using digital twins also can assist in developing real-time analytics applications. The streaming service now enables digital twins to both generate and analyze telemetry. For example, developers can model a fleet of trucks with digital twins that send telemetry to additional digital twins which implement real-time analytics for tracking the fleet, identifying issues, and evaluating performance. The streaming service also can record telemetry from live devices and replay it in simulation to assist in performing offline analytics.

“We are excited to extend the capabilities of our streaming service to harness digital twins for both simulation and real-time analytics,” said Dr. William Bain, ScaleOut Software’s CEO and founder. “By hosting digital twins using our highly scalable, in-memory computing technology, we enable our customers to simulate large systems with complex interactions and gain insights not previously possible.”

Managers can also use ScaleOut Software’s simulation capabilities to model thousands of interacting data sources and predict likely outcomes faster than real time. For example, they can model the effect of delays for an airline’s flight schedule within a multi-hour, forward-looking window to predict and mitigate impacts on passengers caused by weather and equipment issues. This predictive capability can assist in decision making during real-time operations.

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

• Build Simulations at Scale: Now developers can construct simulations that model thousands of devices and other data sources. Fast, in-memory computing ensures high throughput and seamless scaling during simulations with built-in high availability.

• Model with Digital Twins: Developers can harness the popular digital twin model to describe various physical entities using a powerful but easy to use, object-oriented representation of each device or data source. The ScaleOut Digital Twin Builder Toolkit™ lets developers construct digital twin models in both Java and C#.

• Combine Modeling and Analytics: The ScaleOut Digital Twin Streaming Service now helps managers use digital twins to both analyze telemetry from real-world data sources and to model those data sources. This simplifies the creation of workload generators for evaluating real-time analytics. The platform also allows a single digital twin instance to both simulate a data source’s behavior and analyze its telemetry.

• Capture and Replay Live Telemetry: The streaming service can capture telemetry from live data sources and replay it in simulation. This capability streamlines workload generation for simulations and enables offline analysis of live scenarios.

• Make Predictions with Simulation: The streaming service can configure simulations to run faster than real-time. This aids decision making for operational managers by enabling simulations to predict future events based on parameterizable scenarios.

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