Abstract: An incident clustering system for detecting clusters among incident reports can include a data intake module configured to receive incident text strings associated with respective incident reports input by a user, a pre-processing module operatively connected to the data intake module to receive the incident text strings from the data intake module and to pre-process the incident text strings to output pre-processed text strings associated with the respective incident reports, and a token module operatively connected to the pre-processing module to receive the pre-processed text strings. The token module can be configured to identify one or more phrases-of-interest having a plurality of words in the pre-processed text strings and concatenate the plurality of words of each of one or more phrases-of-interest to output concatenated tokens associated with the respective incident reports.

Abstract: A computer-implemented method and system for determining a probability of incident occurrence resulting from one or more changes to one or more computer networked applications. A machine learning (ML) model is trained to identify probability of likelihood of one or more incidents from occurring to one or more computer applications attributable to one or more changes to the one or more computer applications. Information is received corresponding to at least one application change attribute, which is then analyzed by the trained ML model to identify one or more incident occurrence indicators applicable to the at least one application change attribute. A probability of one or more incidents occurring is then determined corresponding to the at least one application change attribute.

Abstract: A computer implemented method and system for detecting one or more anomaly conditions in one or more computer devices. A Machine Learning (ML) model is trained for each of the one or more computer devices to determine threshold operating values for time-based metric data associated with each of the one or more computer devices. Utilizing the trained ML model, time-based metric data is compared for each of the one or more computer devices to the determined threshold operating values to determine if the time-based metric data falls outside of the determined threshold operating values. Provided is notification of an anomaly condition for a computer device responsive to determining the time-based metric data falls outside of the determined threshold operating values associated with the computer device.