Abstract: A method and system for providing an enriched root cause of an incident using machine-generated textual data. The method includes extracting, from a dataset including machine-generated textual data for a monitored environment, a plurality of features related to a root cause of an incident in the monitored environment; generating a suitability score for each of a plurality of insights with respect to the incident based on the extracted features and a suitability model, wherein the suitability model is created based on a training set including a plurality of training inputs and a plurality of training outputs, wherein each training output corresponds to at least one of the plurality of training inputs; and selecting at least one suitable insight based on the generated suitability scores.

Abstract: A system and method for predictive ticketing in information technology (IT) systems. The method includes extracting a plurality of features from monitoring data related to an IT system, wherein the plurality of features includes at least one incident parameter, wherein the monitoring data includes machine-generated textual data; applying a machine learning model to the extracted plurality of features, wherein the machine learning model is configured to output a suitable insight for an incident represented by the at least one incident parameter, wherein the suitable insight is selected from among a plurality of historical insights; and generating a predictive ticket based on the suitable insight, wherein the predictive ticket includes a textual description of an expected future symptom in the IT system.

Abstract: A method and system for providing an enriched root cause of an incident using machine-generated textual data. The method includes extracting, from a dataset including machine-generated textual data for a monitored environment, a plurality of features related to a root cause of an incident in the monitored environment; generating a suitability score for each of a plurality of insights with respect to the incident based on the extracted features and a suitability model, wherein the suitability model is created based on a training set including a plurality of training inputs and a plurality of training outputs, wherein each training output corresponds to at least one of the plurality of training inputs; and selecting at least one suitable insight based on the generated suitability scores.

Abstract: A method and system for determining root-causes of incidences using machine-generated textual data. The method comprises receiving machine-generated textual data from at least one data source; classifying the received machine-generated textual data into at least one statistical metric; processing the statistical metric to recognize a plurality of incidence patterns; correlating the plurality of incidence patterns to identify at least a root-cause of an incidence that occurred in a monitored environment; and generating an alert indicating at least the identified root-cause.

Abstract: A system and method for classifying machine-generated textual data into statistical metrics are determined. The system comprises receiving machine-generated textual data from at least one data source; grouping the machine-generated textual data into a plurality of events; processing each event to determine a plurality of elements embedded therein; determining a type of each of the plurality of elements; and determining a statistical metric for each element based on at least on the type of the element.