Abstract: Machine logic (for example, software) for automatic detection of a probable error in the output of a stream processing model. The detection of this probable error leads to the taking of a responsive action, which, generally speaking, may be one of two types of responsive action: (i) automatically notifying a human individual of the probable error; and/or (ii) automatically taking corrective action (for example, retraining of the model, automatically switching to a redundant backup sensor) without substantial human intervention. In some cases, the probable error is caused by a faulty sensor, which means that retraining will not fix the detected error. In some cases, the probable error is caused by a new trend in the data, which means that retraining on newer data can fix the detected error.

Abstract: Machine logic (for example, software) for automatic detection of a probable error in the output of a stream processing model. The detection of this probable error leads to the taking of a responsive action, which, generally speaking, may be one of two types of responsive action: (i) automatically notifying a human individual of the probable error; and/or (ii) automatically taking corrective action (for example, retraining of the model, automatically switching to a redundant backup sensor) without substantial human intervention. In some cases, the probable error is caused by a faulty sensor, which means that retraining will not fix the detected error. In some cases, the probable error is caused by a new trend in the data, which means that retraining on newer data can fix the detected error.