The future of dual digital technology

Dr. William Payne, CEO and founder of ScaleOut Software, explains what the future of dual-digital technology will require for widespread market adoption.

Traditional stream processing and complex event processing systems do an excellent job of extracting patterns from incoming telemetry, but they make tracking dynamic information about individual data sources difficult and inefficient. As a result, it can be difficult to fully analyze what incoming telemetry says about the state of a living system and take effective action in a timely manner.

This is where the power of real-time digital twins comes in. While dual-digital models have been used for decades in product lifecycle management to aid the design of new devices, they have only recently been applied to state-of-the-art flow processing. advances in scalable in-memory computing devised by my company, ScaleOutI helped enable digital twins to make this possible.

We launched a file ScaleOut Digital Twin Streaming Service™ Two years ago to offer simple and intuitive technology to track critical, dynamically evolving state information about individual IoT data sources and use this information to perform real-time analytics for telemetry. Using state information for each device allows you to deeply reflect on each device in milliseconds and get more effective feedback than was previously possible.

In the past two years, we have applied the digital twin model to many use cases in widely different applications. These technologies include informatics, logistics, physical security alerting, contact tracing, device tracking, industrial control monitoring, cloned license plate detection, and airline system tracking—among others. Building applications for these use cases demonstrated the power of the dual digital model in creating useful flow analytics that can be easily scaled up to track large numbers of data sources. Since its inception, we have continued to enhance this unique platform with new capabilities.

For example, we created a rules engine to implement logic within the digital twin so that new models can be created without the need for specialized programming expertise. Then we added machine learning to our dual numerical models using Microsoft’s ML.NET library. This enables digital twins to look for patterns in telemetry that are difficult for humans to see or identify using code. Recently, we combined our dual digital model with Microsoft’s Azure Digital Twins platform to accelerate real-time processing using in-memory computing technology while providing new visualization and persistence capabilities.

Our digital twin model has also evolved along with the needs of developers. Recently, I sat down with my team to consider what the future of this technology would require for widespread adoption in the market. We found the following:

Incorporating timers to improve the digital twin alarm: What if digital twins combined timers to detect lost or delayed messages from IoT devices? These timers can execute code that signals alerts when needed. This will be necessary to identify failed or unreliable devices in live applications, such as smoke detectors and security sensors.

  • Improving portability with .NET 6 and C#Linux is the most popular and widely used open source operating system that has grown in popularity over several years. By using .NET 6 to build C# numerical binary models, developers can target Windows and Linux systems at the same time, thus increasing C#’s portability and maintaining parity with Java.
  • Allow bulk startup of Digital Twin applications: Our dual digital platform was initially designed to automatically create digital twins when you receive telemetry from a new data source (usually, a device). However, our experience has shown us that many applications need to pre-create a full set of duplex digital instances in order to detect missing devices or for other reasons, such as initializing a complex duplex numeric hierarchy. What if we could create and configure digital twins using file-based data instead of waiting for incoming telemetry?
  • Add simulation capabilities: Our experience building applications has shown that it is necessary to use simulation to test and demonstrate the capabilities of digital twins in analytics flow. In a variety of industries, simulations help us gauge how well digital twins provide answers in real time before they are deployed to live systems. Using simulations will help validate designs and save time and money on implementations.

I am happy to say that our team has achieved many of these goals in our latest edition Version 2 of ScaleOut Digital Twin Streaming Service™. This release provides exciting new features for building digital binary models that meet the needs of real-world applications, and we plan to add support for simulation in an upcoming release. All these features were driven by the requirements that came up during the development of the app. This approach aligns with our design philosophy of starting with a simple, coherent model and then carefully refining it as we learn from real-world experience.

After more than two years creating real-world apps with digital twins, we’ve confirmed the leverage they provide in streaming analytics. As digital twins combine telemetry, state information, and application logic for each physical device, they enable deep introspection, tracking of evolving behavior, and enabling feedback. By taking advantage of scalable in-memory computing, the streaming platform can achieve all of this with an extraordinarily simple and efficient programming model that allows applications to focus on analytics code execution while deferring data visualization challenges and scaling throughput to the platform. This technology could change the way we monitor and interact with large sets of data sources. It has been exciting to watch digital twins tackle challenges in a variety of applications. How will you develop in the next two years?

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