A computer controlled method of operating a training tool for classifying annotated events in content of data stream

A computer controlled method of operating a training tool for classifying annotated events in content of a data stream, the data stream comprising a plurality of parameters, the method comprising the steps of:

• detecting, by the computer, trigger features from variations in parameters of the data stream;
• identifying, by the computer, associated trigger features as separators;
• determining, by the computer, descriptors identifying parameter values corresponding to the separators;
• and
• outputting, by the computer, the separators and corresponding descriptors as training samples, positively or negatively indicative of annotated events depending on positions of the separators in the data stream, wherein a number of the separators is determined, by the computer, for obtaining a balanced set of positive and negative training samples.

A computer controlled training tool for classifying annotated events in content of a data stream, the data stream comprising a plurality of parameters, the computer configured to perform the steps of:

• detecting trigger features from variations in parameters of the data stream;
• identifying associated trigger features as separators;
• determining descriptors identifying parameter values corresponding to the separators;
• and
• outputting the separators and corresponding descriptors as training samples, positively or negatively indicative of annotated events depending on positions of the separators in the data stream, wherein a number of the separators is determined for obtaining a balanced set of positive and negative training samples.