Common use of Analytics Services Clause in Contracts

Analytics Services. Analytics Services are available via their respective MindSphere APIs and provide basic and advanced analytical functions for time series data such as Anomaly Detection, Event Analytics, KPI Calculation, Signal Calculation, Signal Validation and Trend Prediction. They can be utilized in an interactive mode e.g. via the Visual Flow Creator (workflow tool for calling APIs) or from your Applications. • Anomaly Detection aims to support the detection of unexpected behavior of processes and assets. For the training of anomaly detection, normal data is sufficient. Normal data represents the standard conditions of assets. Furthermore, clustering based on anomaly detection techniques allow human interaction and integration of domain knowledge (e.g. by labeling of new clusters and/or anomalies). A developer can build Applications for process and condition monitoring, early warning functionality and detection of fault conditions without explicit definitions. • Event Analytics provides a statistical analysis for visualizing the most frequent events over a period of time. • KPI Calculation offers an easy way to provide various calculations for key performance indicators based on sensor data as well as sequence of events (i.e. from control/automation systems). The characteristic is related to an ISO 3977-9:1999 standard which is in fact dedicated to gas turbines. The characteristic is also applicable to other industrial applications. It is possible to provide automated annotation for time series data for many common characteristics. Additionally, the function can combine two information sources, numerical sensor data as well as events. The Service can be applied for historical data as well as the automated processing of incoming new data. • Signal Calculation offers commonly used missing value handling strategies, for instance, removal and interpolation. It calculates a descriptive summary of a sequence of signal values and if required, it derives new signal values by shifting, smoothing and transforming the original ones. • Signal Validation provides functions that help to detect common issues in time series data. Signal Validation can be used for optimizing the data quality. • Trend Prediction is a forecasting framework that may be useful in the area of process and condition monitoring. Also, seasonality and trend removal is an essential task of data analytics pre-processing.

Analytics Services. Analytics Services are available via their respective MindSphere APIs and provide basic and advanced analytical functions for time series data such as Anomaly Detection, Event Analytics, KPI Calculation, Signal Calculation, Signal Validation and Trend Prediction. They can be utilized in an interactive mode e.g. via the Visual Flow Creator (workflow tool for calling APIs) or from your Applications. •  Anomaly Detection aims to support the detection of unexpected behavior of processes and assets. For the training of anomaly detection, normal data is sufficient. Normal data represents the standard conditions of assets. Furthermore, clustering based on anomaly detection techniques allow human interaction and integration of domain knowledge (e.g. by labeling of new clusters and/or anomalies). A developer can build Applications for process and condition monitoring, early warning functionality and detection of fault conditions without explicit definitions. •  Event Analytics provides a statistical analysis for visualizing the most frequent events over a period of time. •  KPI Calculation offers an easy way to provide various calculations for key performance indicators based on sensor data as well as sequence of events (i.e. from control/automation systems). The characteristic is related to an ISO 3977-9:1999 standard which is in fact dedicated to gas turbines. The characteristic is also applicable to other industrial applications. It is possible to provide automated annotation for time series data for many common characteristics. Additionally, the function can combine two information sources, numerical sensor data as well as events. The Service can be applied for historical data as well as the automated processing of incoming new data. •  Signal Calculation offers commonly used missing value handling strategies, for instance, removal and interpolation. It calculates a descriptive summary of a sequence of signal values and if required, it derives new signal values by shifting, smoothing and transforming the original ones. •  Signal Validation provides functions that help to detect common issues in time series data. Signal Validation can be used for optimizing the data quality. •  Trend Prediction is a forecasting framework that may be useful in the area of process and condition monitoring. Also, seasonality and trend removal is an essential task of data analytics pre-processing.

Analytics Services. Analytics Services are available via their respective MindSphere APIs and provide basic and advanced analytical functions for time series data such as Anomaly Detection, Event Analytics, KPI Calculation, Signal Calculation, Signal Validation and Trend Prediction. They can be utilized in an interactive mode e.g. via the Visual Flow Creator (workflow tool for calling APIs) or from your Applications. • · Anomaly Detection aims to support the detection of unexpected behavior of processes and assets. For the training of anomaly detection, normal data is sufficient. Normal data represents the standard conditions of assets. Furthermore, clustering based on anomaly detection techniques allow human interaction and integration of domain knowledge (e.g. by labeling of new clusters and/or anomalies). A developer can build Applications for process and condition monitoring, early warning functionality and detection of fault conditions without explicit definitions. • · Event Analytics provides a statistical analysis for visualizing the most frequent events over a period of time. • · KPI Calculation offers an easy way to provide various calculations for key performance indicators based on sensor data as well as sequence of events (i.e. from control/automation systems). The characteristic is related to an ISO 3977-9:1999 standard which is in fact dedicated to gas turbines. The characteristic is also applicable to other industrial applications. It is possible to provide automated annotation for time series data for many common characteristics. Additionally, the function can combine two information sources, numerical sensor data as well as events. The Service can be applied for historical data as well as the automated processing of incoming new data. • · Signal Calculation offers commonly used missing value handling strategies, for instance, removal and interpolation. It calculates a descriptive summary of a sequence of signal values and if required, it derives new signal values by shifting, smoothing and transforming the original ones. • · Signal Validation provides functions that help to detect common issues in time series data. Signal Validation can be used for optimizing the data quality. • · Trend Prediction is a forecasting framework that may be useful in the area of process and condition monitoring. Also, seasonality and trend removal is an essential task of data analytics pre-processing.