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ScaleOut Software Announces Machine Learning Capabilities for its Streaming Service

Organizations can now use no-code machine learning for spike, trend, and anomaly detection.

Note: TDWI’s editors carefully choose vendor-issued press releases about new or upgraded products and services. We have edited and/or condensed this release to highlight key features but make no claims as to the accuracy of the vendor's statements.

ScaleOut Software has released major extensions to its Digital Twin Streaming Service that enable real-time digital twin software to implement and host machine learning (ML) and statistical analysis algorithms that immediately identify unexpected behaviors in incoming telemetry. Real-time digital twins can now make extensive use of Microsoft’s ML.NET machine learning library to implement these capabilities for virtually any 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 behaviors and generate alerts.

By harnessing the no-code ScaleOut Model Development Tool, a real-time digital twin can be enhanced to automatically analyze incoming telemetry messages using ML techniques. Machine learning provides important real-time insights that enhance situational awareness and enable fast, effective responses. The tool provides three configuration options for analyzing 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
  • Trend detection: 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
  • Multivariable anomaly detection: Tracks a set of related parameters in aggregate to identify anomalies using a user-selected machine-learning algorithm that performs binary classification with supervised learning

Once configured through the 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.

Benefits of ScaleOut’s Real-Time Digital Twins with Machine Learning

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

  • New capabilities for tracking data sources: Using machine learning dramatically enhances the ability of streaming analytics running in real-time 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 analyzed in real time with fast, scalable performance.
  • Fast, easy application deployment: With the ScaleOut Model Development Tool, these new ML 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 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, and financial services.

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