Abstract: A method for identifying and projecting recurrent event patterns in an Information Technology (IT) infrastructure is disclosed. The method includes deriving at least one time period based on historical events data; grouping the historical events data comprising a plurality of events based on the at least one time period to create a plurality of data points sets; creating an event corpus comprising each of the plurality of events arranged based on frequency of occurrence across the plurality of data points sets; identifying one or more events that have highest frequency of occurrence within the event corpus; determining one or more data points sets from the plurality of data points sets in which the one or more events have occurred; and computing a projected frequency of occurrence of the one or more events in future time periods.

Abstract: System and method for optimizing event alerts in an information technology (IT) infrastructure management system are disclosed. In one embodiment, the method comprises tracking one or more event alerts triggered by monitoring tools. The method further comprises determining a probable usage for at least one configuration item associated with the one or more event alerts. The method further comprises determining a probable threshold value for each of the at least one configuration item based on the probable usage and capacity data. The method further comprises computing a final threshold value for each of the at least one configuration item based on the probable threshold value, seasonality data, or semantic context data. The method further comprises optimizing the event alerts by filtering at least one unwanted event alert from the one or more event alerts based on at least one of the final threshold value and filtering rules.

Abstract: A system and method for classifying and resolving software production incident tickets includes receiving an incident ticket, extracting a plurality of keywords from the incident ticket, and deriving a query vector corresponding to the incident ticket based on the plurality of keywords. The system and method further comprises classifying the incident ticket into at least one of a positive mechanization incident ticket and a negative mechanization incident ticket based on a comparison of the query vector and a plurality of vectors derived from a plurality of past incident tickets. The plurality of vectors are derived based on a plurality of keywords and their corresponding occurrences in the plurality of past incident tickets.

Abstract: A method for identifying and projecting recurrent event patterns in an Information Technology (IT) infrastructure is disclosed. The method includes deriving at least one time period based on historical events data; grouping the historical events data comprising a plurality of events based on the at least one time period to create a plurality of data points sets; creating an event corpus comprising each of the plurality of events arranged based on frequency of occurrence across the plurality of data points sets; identifying one or more events that have highest frequency of occurrence within the event corpus; determining one or more data points sets from the plurality of data points sets in which the one or more events have occurred; and computing a projected frequency of occurrence of the one or more events in future time periods.

Abstract: A system and method for classifying and resolving software production incident tickets includes receiving an incident ticket, extracting a plurality of keywords from the incident ticket, and deriving a query vector corresponding to the incident ticket based on the plurality of keywords. The system and method further comprises classifying the incident ticket into at least one of a positive mechanization incident ticket and a negative mechanization incident ticket based on a comparison of the query vector and a plurality of vectors derived from a plurality of past incident tickets. The plurality of vectors are derived based on a plurality of keywords and their corresponding occurrences in the plurality of past incident tickets.

Abstract: A system and method for classifying and resolving software production incident tickets includes receiving an incident ticket, extracting a plurality of keywords from the incident ticket, and deriving a query vector corresponding to the incident ticket based on the plurality of keywords. The system and method further comprises classifying the incident ticket into at least one of a positive mechanization incident ticket and a negative mechanization incident ticket based on a comparison of the query vector and a plurality of vectors derived from a plurality of past incident tickets. The plurality of vectors are derived based on a plurality of keywords and their corresponding occurrences in the plurality of past incident tickets.

Abstract: System and method for optimizing event alerts in an information technology (IT) infrastructure management system are disclosed. In one embodiment, the method comprises tracking one or more event alerts triggered by monitoring tools. The method further comprises determining a probable usage for at least one configuration item associated with the one or more event alerts. The method further comprises determining a probable threshold value for each of the at least one configuration item based on the probable usage and capacity data. The method further comprises computing a final threshold value for each of the at least one configuration item based on the probable threshold value, seasonality data, or semantic context data. The method further comprises optimizing the event alerts by filtering at least one unwanted event alert from the one or more event alerts based on at least one of the final threshold value and filtering rules.

Abstract: System and method for predicting an event in an information technology (IT) infrastructure are disclosed. In one embodiment, the method comprises obtaining unstructured input data from a SCK database and deriving a seasonality activation function and a capacity activation function by analyzing the unstructured input data. The method further comprises converting the unstructured input data into at least one time series data comprising a plurality of data points. Each of the plurality of data points is separated by a predefined time interval. The method further comprises calculating a moving average for each of the plurality data points and calculating a weighted moving average by aggregating the moving average calculated for each of the plurality of data points based on a predetermined weight. The method further comprises predicting occurrence of the event based on the weighted moving average, the seasonality activation function, and the capacity activation function.