Abstract: A Multi-site Integrated Session-oriented Transaction (MIST) computing system includes a MIST mapper configured to process a compiled distributed compute session request to determine, for each of at least one remote sets of data, whether to process the remote set of data with one of one or more field nodes or with a cloud node based on a cost of transporting the remote set of data from the proximate field node to the cloud node. In response to determining to process the remote set of data with the cloud node, the MIST mapper is configured to output a data retrieval request to the proximate field node. In response to determining to process the remote set of data with the field node, the MIST mapper is configured to output the computation request to the proximate field node.

Abstract: A processing pipeline for supporting machine-learning processes for network monitoring and information management as well as specific analytics for particular use cases. The processing pipeline 500 takes in system data (502) and pre-processes (504) the system data. The system data (502) may include any of the types of data described above including text log files, and categorical data from various sources. The illustrated processing pipeline 500 includes two branches; a data fitting branch (506) where a model is developed for the data and a data transformation branch (508) where the developed model is leveraged to transform live data. For certain event detection use cases, the output of the data transformation branch (508) includes a score (510) (e.g., a threat level score) and an attribution (512).

Abstract: Utilities (e.g., methods, systems, apparatuses, etc.) for use in generating and making use of priority scores for data generated by one or more data systems that more accurately prioritize those events and other pieces of data to be addressed by analysts and troubleshooters before others (e.g., collectively taking into account threats posed by origin host components and risks to impacted host components) to work the highest risk events and alarms first and to effectively and efficiently spend their alarm monitoring time.

Abstract: An advanced intelligence engine (AIE) for use in identifying what may be complex events or developments on one or more data platforms or networks from various types of structured or normalized data generated by one or more disparate data sources. The AIE may conduct one or more types of quantitative, correlative, behavioral and corroborative analyses to detect events from what may otherwise be considered unimportant or non-relevant information spanning one or more time periods. Events generated by the AIE may be passed to an event manager to determine whether further action is required such as reporting, remediation, and the like.

Abstract: A processing pipeline for supporting advanced analytics for network monitoring and information management as well as specific analytics for particular use cases. The processing pipeline 500 takes in system data (502) and pre-processes (504) the system data. The system data (502) may include any of the types of data described above including text log files, and categorical data from various sources. The illustrated processing pipeline 500 includes two branches; a data modeling branch (506) where a model is developed for the data and a data evaluation branch (508) where the developed model is leveraged to evaluate live data. For certain event detection use cases, the output of the data evaluation branch (508) includes a score (510) (e.g., a threat level score) and context information for evaluating the threat.

Abstract: Utilities (e.g., methods, systems, apparatuses, etc.) for use in generating and making use of priority scores for data generated by one or more data systems that more accurately prioritize those events and other pieces of data to be addressed by analysts and troubleshooters before others (e.g., collectively taking into account threats posed by origin host components and risks to impacted host components) to work the highest risk events and alarms first and to effectively and efficiently spend their alarm monitoring time.

Abstract: An advanced intelligence engine (AIE) for use in identifying what may be complex events or developments on one or more data platforms or networks from various types of structured or normalized data generated by one or more disparate data sources. The AIE may conduct one or more types of quantitative, correlative, behavioral and corroborative analyses to detect events from what may otherwise be considered unimportant or non-relevant information spanning one or more time periods. Events generated by the AIE may be passed to an event manager to determine whether further action is required such as reporting, remediation, and the like.

Abstract: Tools for use in obtaining useful information from processed log messages generated by a variety of network platforms (e.g., Windows servers, Linux servers, UNIX servers, databases, workstations, etc.). The log messages may be processed by one or more processing platforms or “log managers” using any appropriate rule base to identify “events” (i.e., log messages of somewhat heightened importance), and one or more “event managers” may analyze the events to determine whether alarms should be generated therefrom. The tools may be accessed via any appropriate user interface of a console that is in communication with the various log managers, event managers, etc., to perform numerous tasks in relation to logs, events and alarms.

Abstract: Utilities (e.g., methods, systems, apparatuses, etc.) for use in generating and making use of priority scores for data generated by one or more data systems that more accurately prioritize those events and other pieces of data to be addressed by analysts and troubleshooters before others (e.g., collectively taking into account threats posed by origin host components and risks to impacted host components) to work the highest risk events and alarms first and to effectively and efficiently spend their alarm monitoring time.

Abstract: An advanced intelligence engine (AIE) for use in identifying what may be complex events or developments on one or more data platforms or networks from various types of structured or normalized data generated by one or more disparate data sources. The AIE may conduct one or more types of quantitative, correlative, behavioral and corroborative analyses to detect events from what may otherwise be considered unimportant or non-relevant information spanning one or more time periods. Events generated by the AIE may be passed to an event manager to determine whether further action is required such as reporting, remediation, and the like.

Abstract: An advanced intelligence engine (AIE) for use in identifying what may be complex events or developments on one or more data platforms or networks from various types of structured or normalized data generated by one or more disparate data sources. The AIE may conduct one or more types of quantitative, correlative, behavioral and corroborative analyses to detect events from what may otherwise be considered unimportant or non-relevant information spanning one or more time periods. Events generated by the AIE may be passed to an event manager to determine whether further action is required such as reporting, remediation, and the like.

Abstract: Tools for use in obtaining useful information from processed log messages generated by a variety of network platforms (e.g., Windows servers, Linux servers, UNIX servers, databases, workstations, etc.). The log messages may be processed by one or more processing platforms or “log managers” using any appropriate rule base to identify “events” (i.e., log messages of somewhat heightened importance), and one or more “event managers” may analyze the events to determine whether alarms should be generated therefrom. The tools may be accessed via any appropriate user interface of a console that is in communication with the various log managers, event managers, etc., to perform numerous tasks in relation to logs, events and alarms.

Abstract: An advanced intelligence engine (AIE) for use in identifying what may be complex events or developments on one or more data platforms or networks from various types of structured or normalized data generated by one or more disparate data sources. The AIE may conduct one or more types of quantitative, correlative, behavioral and corroborative analyses to detect events from what may otherwise be considered unimportant or non-relevant information spanning one or more time periods. Events generated by the AIE may be passed to an event manager to determine whether further action is required such as reporting, remediation, and the like.

Abstract: An advanced intelligence engine (AIE) for use in identifying what may be complex events or developments on one or more data platforms or networks from various types of structured or normalized data generated by one or more disparate data sources. The AIE may conduct one or more types of quantitative, correlative, behavioral and corroborative analyses to detect events from what may otherwise be considered unimportant or non-relevant information spanning one or more time periods. Events generated by the AIE may be passed to an event manager to determine whether further action is required such as reporting, remediation, and the like.

Abstract: Tools for use in obtaining useful information from processed log messages generated by a variety of network platforms (e.g., Windows servers, Linux servers, UNIX servers, databases, workstations, etc.). The log messages may be processed by one or more processing engines (e.g., “log managers”) using any appropriate rule base to identify “events” (i.e., log messages of somewhat heightened importance), and one or more “event managers” may analyze the events to determine whether alarms should be generated therefrom. The tools may be accessed via any appropriate user interface of a console that is in communication with the various log managers, event managers, etc., to perform numerous tasks in relation to logs, events and alarms.

Abstract: An advanced intelligence engine (AIE) for use in identifying what may be complex events or developments on one or more data platforms or networks from various types of structured or normalized data generated by one or more disparate data sources. The AIE may conduct one or more types of quantitative, correlative, behavioral and corroborative analyses to detect events from what may otherwise be considered unimportant or non-relevant information spanning one or more time periods. Events generated by the AIE may be passed to an event manager to determine whether further action is required such as reporting, remediation, and the like.

Abstract: Tools for use in obtaining useful information from processed log messages generated by a variety of network platforms (e.g., Windows servers, Linux servers, UNIX servers, databases, workstations, etc.). The log messages may be processed by one or more processing platforms or “log managers” using any appropriate rule base to identify “events” (i.e., log messages of somewhat heightened importance), and one or more “event managers” may analyze the events to determine whether alarms should be generated therefrom. The tools may be accessed via any appropriate user interface of a console that is in communication with the various log managers, event managers, etc., to perform numerous tasks in relation to logs, events and alarms.

Abstract: Tools for use in obtaining useful information from processed log messages generated by a variety of network platforms (e.g., Windows servers, Linux servers, UNIX servers, databases, workstations, etc.). The log messages may be processed by one or more processing platforms or “log managers” using any appropriate rule base to identify “events” (i.e., log messages of somewhat heightened importance), and one or more “event managers” may analyze the events to determine whether alarms should be generated therefrom. The tools may be accessed via any appropriate user interface of a console that is in communication with the various log managers, event managers, etc., to perform numerous tasks in relation to logs, events and alarms.

Abstract: The present invention generally relates to log message processing such that events can be detected and alarms can be generated. For example, log messages are generated by a variety of network platforms (e.g., Windows servers, Linux servers, UNIX servers, databases, workstations, etc.). Often, relatively large numbers of logs are generated from these platforms in different formats. A log manager described herein collects such log data using various protocols (e.g., Syslog, SNMP, SMTP, etc.) to determine events. That is, the log manager may communicate with the network platforms using appropriate protocols to collect log messages therefrom. The log manager may then determine events (e.g., unauthorized access, logins, etc.) from the log data and transfer the events to an event manager. The event manager may analyze the events and determine whether alarms should be generated therefrom.

Abstract: The present invention generally relates to log message processing such that events can be detected and alarms can be generated. For example, log messages are generated by a variety of network platforms (e.g., Windows servers, Linux servers, UNIX servers, databases, workstations, etc.). Often, relatively large numbers of logs are generated from these platforms in different formats. A log manager described herein collects such log data using various protocols (e.g., Syslog, SNMP, SMTP, etc.) to determine events. That is, the log manager may communicate with the network platforms using appropriate protocols to collect log messages therefrom. The log manager may then determine events (e.g., unauthorized access, logins, etc.) from the log data and transfer the events to an event manager. The event manager may analyze the events and determine whether alarms should be generated therefrom.