New TrendMiner Release Democratizes Machine Learning

May 10, 2021
Democratization of machine learning makes operational experts more flexible to improve and predict process and asset performance.

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Software AG’s TrendMiner releases TrendMiner 2021.R2. The new release extends the reach of notebook integration, allowing analytics expert-users to make their data model outputs available to the rest of the organization. The new multi-variate anomaly detection model allows optimal process conditions to be trained on historical data and the model to detect anomalies on new incoming data. TrendMiner 2021.R2 also allows self-service integration via This enables contextual process information from other business applications to be taken into account and workflows in external systems to be triggered through the new anomaly detection model.

TrendMiner enables operational experts in process manufacturing industries to analyze, monitor and predict operational performance using sensor-generated time-series data. The goal of TrendMiner has always been to empower engineers with analytics for improving operational excellence without the need to rely on data scientists, according to the company. It brings data science to the engineers.

TrendMiner 2021.R2 extends the notebook capabilities of the previous release, enabling them to be operationalized by deploying custom-created data models into an embedded scoring/inference engine through use of machine learning model tags. The machine learning model tags are available for all TrendMiner users, as if they were tags originating from an enterprise historian or any other time-series data source. All existing TrendMiner capabilities can be applied, such as visualizing recent and historical data, searching for patterns or threshold values as well as monitoring using the machine learning model patterns.

The TrendMiner 2021.R2 release now offers a proprietary model for multi-variate anomaly detection via the mentioned notebook and ‘Machine Learning Model’ tags functionality. The TrendMiner Anomaly Detection Model can be trained on a trend view containing normal operating conditions of the process. After learning the desired process conditions, the model will then be able to detect anomalies on new incoming data. The model will return information as to whether a new datapoint is an outlier or not based on a given threshold (anomaly class) or return an anomaly score. The higher the anomaly score, the more likely it is that the datapoint is an outlier.

Factories today are capturing and storing an enormous amount of data directly or indirectly related to the production process. All this data typically ends up in best-of-breed business applications serving specific operational purposes. This contextual information residing in various business applications can give new insights for improving operational performance if the operational experts can actually access that data. With the introduction of the integration add-on powered by within the TrendMiner platform, engineers can now create integrations to crucial business applications themselves. The self-service integration via allows workflows to be created across the business applications’ on-premises and in-cloud solutions. This can, for example, be used to notify colleagues with a Microsoft Teams or Slack message and to simultaneously add a maintenance work request in SAP when a TrendMiner monitor fires an alert.

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