Abstract: Methods, systems, and computer-readable storage media for defining an autoencoder architecture including a neural network, during training of the autoencoder, recording a loss value at each iteration to provide a plurality of loss values, the autoencoder being trained using a data set that is associated with a domain, and a learning rate to provide a trained autoencoder, calculating a penalty score using at least a portion of the plurality of loss values, the penalty score being based on a loss span penalty PLS, a convergence penalty PC, and a fluctuation penalty PF, comparing the penalty score P to a threshold penalty score to affect a comparison, and selectively employing the trained autoencoder for anomaly detection within the domain based on the comparison.

Abstract: Methods, systems, and computer-readable storage media for receiving a time-series of data values associated with a plurality of sensors, each sensor generating at least a portion of the time-series of a respective data value, providing a plurality of auto-regression models, each auto-regression model being provided based on a respective first sub-set of the time-series of data values used as input, and a respective second sub-set of the time-series of data values used as training data during a training process, receiving respective data values associated with a time from and generated by each of the plurality of sensors, determining respective predicted values for each of the auto-regression models, and selectively indicating that an anomaly is present in the system based on respective predicted values for each of the auto-regression models, and the respective data values associated with a time.

Abstract: Methods, systems, and computer-readable storage media for defining an autoencoder architecture including a neural network, during training of the autoencoder, recording a loss value at each iteration to provide a plurality of loss values, the autoencoder being trained using a data set that is associated with a domain, and a learning rate to provide a trained autoencoder, calculating a penalty score using at least a portion of the plurality of loss values, the penalty score being based on a loss span penalty PLS, a convergence penalty PC, and a fluctuation penalty PF, comparing the penalty score P to a threshold penalty score to affect a comparison, and selectively employing the trained autoencoder for anomaly detection within the domain based on the comparison.

Abstract: Methods, systems, and computer-readable storage media for receiving a time-series of data values associated with a plurality of sensors, each sensor generating at least a portion of the time-series of a respective data value, providing a plurality of auto-regression models, each auto-regression model being provided based on a respective first sub-set of the time-series of data values used as input, and a respective second sub-set of the time-series of data values used as training data during a training process, receiving respective data values associated with a time from and generated by each of the plurality of sensors, determining respective predicted values for each of the auto-regression models, and selectively indicating that an anomaly is present in the system based on respective predicted values for each of the auto-regression models, and the respective data values associated with a time.

Abstract: Methods and apparatus, including computer program products, are provided for providing for processing calculation plans. In one aspect, there is provided a computer-implemented method. The method may include generating a calculation plan including a plurality of nodes; determining whether at least one of the nodes includes a function node; and compiling the function node into executable code to enable execution of the plurality of nodes including the function node at the database. Related apparatus, systems, methods, and articles are also described.

Abstract: Methods and apparatus, including computer program products, are provided for providing for processing calculation plans. In one aspect, there is provided a computer-implemented method. The method may include generating a calculation plan including a plurality of nodes; determining whether at least one of the nodes includes a function node; and compiling the function node into executable code to enable execution of the plurality of nodes including the function node at the database. Related apparatus, systems, methods, and articles are also described.