ScaleOut reveals the ML capabilities for its digital twin streaming service

William Bain of ScaleOut Software

ScaleOut Software has announced extensions to its ScaleOut Digital Twin Streaming Service . These are said to enable real-time digital twin software to implement and host machine learning and statistical analysis algorithms that immediately identify unexpected behaviours exhibited by incoming telemetry.

Real-time digital twins can now make extensive use of Microsoft’s ML.NET machine learning library to implement these groundbreaking capabilities for virtually any Internet of Things (IoT) device or source object.

Integration of machine learning with real-time digital twins offers powerful new options for real-time monitoring across a wide variety of applications. For example, cloud-based real-time digital twins can track a fleet of trucks to identify subtle changes in key engine parameters with predictive analytics that avoid costly failures. Security monitors tracking perimeter entrances and sound sensors can use machine learning techniques to automatically identify unexpected behaviours and generate alerts.

By harnessing the no-code ScaleOut Model Development Tool, a real-time digital twin can easily be enhanced to automatically analyse incoming telemetry messages using machine learning techniques. Machine learning provides important real-time insights that enhance situational awareness and enable fast, effective responses. The tool provides three configuration options for analysing numeric parameters contained within incoming messages to spot issues as they arise:

  • Spike Detection: Tracks a single parameter from a data source to identify a spike in its values over time using an adaptive kernel density estimation algorithm implemented by ML.NET.
  • Trend Detection: Also tracks a single parameter to identify a trend change, such as an unexpected increase over time for a parameter that is normally stable, using a linear regression algorithm that detects inflection points.
  • Multi-Variable Anomaly Detection: Tracks a set of related parameters in aggregate to identify anomalies using a user-selected machine-learning algorithm implemented by ML.NET that performs binary classification with supervised learning.

Once configured through the ScaleOut Model Development Tool, the ML algorithms run automatically and independently for each data source within their corresponding real-time digital twins as incoming messages are received. Each real-time digital twin can automatically capture anomalous events for follow-up analysis and generate alerts to popular alerting providers, such as Splunk, Slack, and Pager Duty, to support remediation by service or security teams.

“We are excited to offer powerful machine learning capabilities for real-time digital twins that will make it even easier to immediately spot issues or identify opportunities across a large population of data sources,” says Dr. William Bain, ScaleOut Software’s CEO and founder. “ScaleOut Software has built the next step in the evolution of the Microsoft Azure IoT and ML.NET ecosystem, and we look forward to helping our customers harness these technologies to enhance their real-time monitoring and streaming analytics.”

Benefits of the system

Integrating machine learning into ScaleOut’s real-time digital twins offers these key benefits:

  • Powerful New Capabilities for Tracking Data Sources: The use of machine learning dramatically enhances the ability of streaming analytics running in real-time digital twins to automatically predict and identify emerging issues, thereby boosting their effectiveness.
  • Simultaneous Tracking for Thousands of Data Sources: The integration of machine learning with real-time digital twins using in-memory computing techniques enables thousands of data streams to be independently analysed in real-time with fast, scalable performance.
  • Fast, Easy Application Deployment: With the ScaleOut Model Development Tool, these new machine learning capabilities can be configured in minutes using an intuitive GUI. No code development or library integration is required. Applications can optionally take advantage of a fully integrated rules engine to enhance their real-time analytics.
  • Seamless Use of Microsoft’s Powerful Machine Learning Library: Users can automatically take advantage of Microsoft’s technology for machine learning (ML.NET) to enhance their real-time device tracking and streaming analytics.
  • Virtually Unlimited Application: These new capabilities are useful across a wide variety of applications that track numeric telemetry, with use cases including telematics, logistics, security, healthcare, retail, financial services, and many others.

Comment on this article below or via Twitter: @IoTNow_OR @jcIoTnow


9 IoT applications that will change everything

Posted on: September 1, 2021

Whether you are a future-minded CEO, tech-driven CEO or IT leader, you’ve come across the term IoT before. It’s often used alongside superlatives regarding how it will revolutionize the way you work, play, and live. But is it just another buzzword, or is it the as-promised technological holy grail? The truth is that Internet of

Read more

Which IoT Platform 2021? IoT Now Enterprise Buyers’ Guide

Posted on: August 30, 2021

There are several different parts in a complete IoT solution, all of which must work together to get the result needed, write IoT Now Enterprise Buyers’ Guide – Which IoT Platform 2021? authors Robin Duke-Woolley, the CEO and Bill Ingle, a senior analyst, at Beecham Research. Figure 1 shows these parts and, although not all

Read more

CAT-M1 vs NB-IoT – examining the real differences

Posted on: June 21, 2021

As industry players look to provide the next generation of IoT connectivity, two different standards have emerged under release 13 of 3GPP – CAT-M1 and NB-IoT.

Read more

IoT and home automation: What does the future hold?

Posted on: June 10, 2020

Once a dream, iot home automation is slowly but steadily becoming a part of daily lives around the world. In fact, it is believed that the global market for smart home automation will reach $40 billion by 2020.

Read more

Infineon and Rainforest Connection create real-time monitoring system to detect wildfires

Posted on: October 22, 2021

Munich and San Jose, California, 21 October, 2021 – Infineon Technologies AG a provider of semiconductors for mobility, energy efficiency and the IoT, announced a collaboration with Rainforest Connection (RFCx), a non-profit organisation that uses acoustic technology, Big Data and Artificial Intelligence / Machine Learning to save the rainforests and monitor biodiversity.

Read more

Infineon simplifies secure IoT device-to-cloud authentication with CIRRENT Cloud ID service

Posted on: October 21, 2021

Munich, Germany. 21 October 2021 – Infineon Technologies AG launched CIRRENT Cloud ID, a service that automates cloud certificate provisioning and IoT device-to-cloud authentication. The easy-to-use service extends the chain of trust and makes tasks easier and more secure from chip-to-cloud, while lowering companies’ total cost of ownership. Cloud ID is ideal for cloud-connected product companies

Read more