Abstract: A specification of an information technology change is received via an information technology service management system. The specification of the information technology change is analyzed to determine features of the information technology change. Machine-generated data is analyzed to identify a phenomena detected in the machine-generated data. To a machine learning model, the features of the information technology change and features of the detected phenomena in the machine-generated data are provided to determine a correlation between the information technology change and the detected phenomena in the machine-generated data.

Abstract: A computer-generated data entry is received. The computer-generated data entry is segmented into a set of tokens. A plurality of different token permutation groupings are determined. Each of the different token permutation groupings includes a different subset of tokens from the set of tokens of the computer-generated data entry. For the computer-generated data entry, a plurality of token permutation grouping identifiers associated with at least a portion of the plurality of different token permutation groupings is obtained. It is determined whether the computer-generated data entry belongs to any data entry cluster among a plurality of previously identified data entry clusters based on a search performed using the token permutation grouping identifiers of the computer-generated data entry.

Abstract: A pre-shared compression dictionary is received. The pre-shared compression dictionary was generated based on an analysis of sample data for use in compression of other data. A compressed version of a batch of machine-generated data is received. The batch of machine-generated data has been compressed at least in part using the pre-shared compression dictionary and a batch-specific compression dictionary. The received compressed batch is uncompressed using the batch-specific compression dictionary to determine an intermediate version. The intermediate version is uncompressed using the pre-shared compression dictionary to determine an uncompressed version of the batch of machine-generated data.

Abstract: A computer-generated data entry is received. The computer-generated data entry is segmented into a set of tokens. A plurality of different token permutation groupings are determined. Each of the different token permutation groupings includes a different subset of tokens from the set of tokens of the computer-generated data entry. For the computer-generated data entry, a corresponding token permutation grouping identifier is determined for each grouping of the plurality of different token permutation groupings. It is determined whether the computer-generated data entry belongs to any data entry cluster among a plurality of previously identified data entry clusters based on a search performed using the token permutation grouping identifiers of the computer-generated data entry.

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 request to explore a set of log entries is received. The set of log entries are analyzed to identify common portions in contents of at least a portion of the set of log entries. Based on the analysis that identified the common portions, filters to explore the set of log entries are automatically recommended.

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 computer-generated data entry is received. The computer-generated data entry is segmented into a set of tokens. A plurality of different token permutation groupings are determined. Each of the different token permutation groupings includes a different subset of tokens from the set of tokens of the computer-generated data entry. For the computer-generated data entry, a corresponding token permutation grouping identifier is determined for each grouping of the plurality of different token permutation groupings. It is determined whether the computer-generated data entry belongs to any data entry cluster among a plurality of previously identified data entry clusters based on a search performed using the token permutation grouping identifiers of the computer-generated data entry.

Abstract: A specification of an information technology change is received via an information technology service management system. The specification of the information technology change is analyzed to determine features of the information technology change. Machine-generated data is analyzed to identify a phenomena detected in the machine-generated data. To a machine learning model, the features of the information technology change and features of the detected phenomena in the machine-generated data are provided to determine a correlation between the information technology change and the detected phenomena in the machine-generated data.

Abstract: Devices, systems, and methods of detecting whether an electronic device or computerized device or computer, is communicating with a computerized service or a trusted server directly and without an intermediary web-proxy, or indirectly by utilizing a proxy server or web-proxy. The system searches for particular characteristics or attributes, that characterize a proxy-based communication session or channel and that do not characterize a direct non-proxy-based communication session or channel; or conversely, the system searches for particular characteristics or attributes, that characterize a direct non-proxy-based communication session or channel and that do not characterize a proxy-based communication session or channel; and based on these characteristics, determines whether or not a proxy server exists and operates.

Abstract: A request to explore a set of log entries is received. The set of log entries are analyzed to identify common portions in contents of at least a portion of the set of log entries. Based on the analysis that identified the common portions, filters to explore the set of log entries are automatically recommended.

Abstract: A pre-shared compression dictionary is received. The pre-shared compression dictionary was generated based on an analysis of sample data for use in compression of other data. A compressed version of a batch of machine-generated data is received. The batch of machine-generated data has been compressed at least in part using the pre-shared compression dictionary and a batch-specific compression dictionary. The received compressed batch is uncompressed using the batch-specific compression dictionary to determine an intermediate version. The intermediate version is uncompressed using the pre-shared compression dictionary to determine an uncompressed version of the batch of machine-generated data.

Abstract: A plurality of correlations is determined including by applying a machine learning model to a first plurality of features extracted from a plurality of information technology and operations management alerts and information technology service management reporting data. Each correlation of the plurality of correlations is between a corresponding one of the plurality of information technology and operations management alerts and at least one corresponding portion of the information technology service management reporting data. The information technology service management reporting data includes at least one urgency indicator. A prioritized list of information technology and operations management alerts is generated based at least in part on the determined plurality of correlations and the at least one urgency indicator. The prioritized list of information technology and operations management alerts is organized based at least in part on relative priorities of the alerts.

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.

Abstract: Devices, systems, and methods of detecting whether an electronic device or computerized device or computer, is communicating with a computerized service or a trusted server directly and without an intermediary web-proxy, or indirectly by utilizing a proxy server or web-proxy. The system searches for particular characteristics or attributes, that characterize a proxy-based communication session or channel and that do not characterize a direct non-proxy-based communication session or channel; or conversely, the system searches for particular characteristics or attributes, that characterize a direct non-proxy-based communication session or channel and that do not characterize a proxy-based communication session or channel; and based on these characteristics, determines whether or not a proxy server exists and operates.

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 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: Devices, systems, and methods of detecting whether an electronic device or computerized device or computer, is communicating with a computerized service or a trusted server directly and without an intermediary web-proxy, or indirectly by utilizing a proxy server or web-proxy. The system searches for particular characteristics or attributes, that characterize a proxy-based communication session or channel and that do not characterize a direct non-proxy-based communication session or channel; or conversely, the system searches for particular characteristics or attributes, that characterize a direct non-proxy-based communication session or channel and that do not characterize a proxy-based communication session or channel; and based on these characteristics, determines whether or not a proxy server exists and operates.

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 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.