Abstract:
A system is provided for decomposing events from managed infrastructures. A first engine is configured to receive message data from a managed infrastructure that includes managed infrastructure physical hardware that supports the flow and processing of information, The at least one engine is configured to determine common characteristics of events and produce clusters of events relating to the failure of errors in the managed infrastructure. Membership in a cluster indicates a common factor of the events that is a failure or an actionable problem in a physical hardware of the managed infrastructure directed to supporting the flow and processing of information. The first engine is configured to create one or more situations that is a collection of one or more events or alerts representative of the actionable problem in the managed infrastructure. A second engine is configured to determine one or more common steps from events and produces clusters relating to events. The second engine determines one or more common characteristics of events and produces clusters of events relating to the failure or errors in the managed infrastructure. The system is configured to use natural language processing techniques to analyze threshold entries in a situation room to identify resolutions to problems.
Abstract:
An event clustering system includes a processor that generates reports. An extraction engine is in communication with an infrastructure. The extraction engine receives data from the infrastructure, produces events and populates a database with a dictionary of event or graph entropy. An alert engine receives the events and creates alerts mapped into a matrix, M. A signalizer engine includes one or more of an NMF engine, a k-means clustering engine and a topology proximity engine. The signalizer engine determines one or more common steps from events and produces clusters relating to the alerts and or events. One or more interactive displays provide a collaborative interface a coupled to the extraction and the signalizer engine for decomposing events from the infrastructure. A reporting engine generates a report from at least one of the clusters and the events that are retrieved from the collaborative interface with a source address for each event to assign a graph coordinate in the graph to the event with an optional subset of attributes being extracted for each event and turning that into a vector of the graph. In response to production of the clusters one or more physical changes in a managed infrastructure hardware is made, and in response.
Abstract:
A system is provided for clustering events. A first engine receives message data from a managed infrastructure that includes managed infrastructure physical hardware which supports the flow and processing of information. A second engine determines common characteristics of events and produces clusters of events relating to the failure of errors in the managed infrastructure. Membership in a cluster indicates a common factor of the events that is a failure or an actionable problem in the physical hardware managed infrastructure directed to supporting the flow and processing of information. Events are produced that relate to the managed infrastructure while converting the events into words and subsets used to group the events that relate to failures or errors in the managed infrastructure, including the managed infrastructure physical hardware. A situation room is provided with a collaborative interface (UI) for decomposing events from managed infrastructures. The (UI) is available by one or more designated individuals relative to one or more failures or errors in a managed infrastructure.
Abstract:
A distributed system includes a client system with a plurality of managed devices. At least one agent is in communication with the managed devices. The one agent updates and changes at least one management policy. Anomaly detection is pushed out to the one agent. A dedicated polling server is in communication with the one agent. The one agent communicates over a subscribed bus, and runs on the dedicated polling server. A portal bridge is in communication with the bus and communicates through a client system firewall to a Network System. The portal bridge listens on the bus through a firewall of the client system. The one agent discovers a local environment and retrieves monitored client system parameters. The one agent performs at least one of: communicates a time data series or detects an anomaly, in response to a detection of a hole the at least one agent checks a value for an anomaly and detected anomalies are communicated to the server, when an anomaly is not detected the agent sends a time series data point to the repository and when there are changes in the monitored system parameters the agent loads the change and restarts with the polling. In response to anomaly detection one or more physical changes in a managed infrastructure hardware is made, where the hardware supports the flow and processing of information, and in response to production of the clusters security of the managed infrastructure is maintained.
Abstract:
Methods and system are provided for decomposing events from managed infrastructures. The system decomposes events from a managed infrastructure and includes a first engine that receives data from a managed infrastructure which includes managed infrastructure physical hardware. The infrastructure physical hardware supports the flow and processing of information. A second engine determines common characteristics of events and produces clusters of events relating to the failure of errors in the managed infrastructure. Membership in a cluster indicates a common factor of the events that is a failure or an actionable problem in the physical hardware managed infrastructure directed to support the flow and processing of information. Events are produced that relate to the managed infrastructure. The events are converted into words and subsets used to group the events that relate to failures or errors in the managed infrastructure, including the managed infrastructure physical hardware. The events have textural context. Semantic meaning is applied to the textual context of the events. A change to a managed infrastructure physical hardware component is made.
Abstract:
An event clustering system is configured to generate reports. An extraction engine is in communication with an infrastructure. The extraction engine in operation receives data from the infrastructure and produces events. An alert engine receives the events and creates alerts mapped into a matrix, M. A sigalizer engine includes one or more of an NMF engine, a k-means clustering engine and a topology proximity engine. The sigalizer engine determines one or more common steps from events and produces clusters relating to the alerts and or events. A reporting engine is configured to be coupled to the event clustering system.
Abstract:
An event clustering system includes an extraction engine in communication with a managed infrastructure. A sigalizer engine that includes one or more of an NMF engine, a k-means clustering engine and a topology proximity engine. The sigalizer engine determines one or more common characteristics or features from events that includes one or more event parameters. The sigalizer engine uses the common features of events to produce clusters of events relating to the failure or errors in the managed infrastructure. Membership in a cluster indicates a common factor of the events that is a failure or an actionable problem in the physical hardware managed infrastructure directed to supporting the flow and processing of information. Each of an event parameter is converted into a numerical representation.
Abstract:
A user interface system has at least a first engine configured to receive message data from managed infrastructure that includes managed infrastructure physical hardware which supports the flow and processing of information. The at least first engine determines common characteristics of events and produces clusters of events relating to the failure of errors in the managed infrastructure. Membership in a cluster indicates a common factor of the events that is a failure or an actionable problem in the physical hardware managed infrastructure directed to supporting the flow and processing of information. One or more situations are created that is a collection of one or more events or alerts representative of the actionable problem in the managed infrastructure. A display computer system generates a dashboard display that includes situations from clustered messages received from managed infrastructure. The display computer system is coupled to or included in a situation room coupled to the at least first engines.
Abstract:
A system is provided for clustering events. At least one engine is configured to receive message data from managed infrastructure that includes managed infrastructure physical hardware which supports the flow and processing of information. The at least one engine is configured to determine common characteristics of events and produce clusters of events relating to the failure of errors in the managed infrastructure. Membership in a cluster indicates a common factor of the events that is a failure or an actionable problem in the physical hardware managed infrastructure directed to supporting the flow and processing of information. The at least one engine is configured to create one or more situations that is a collection of one or more events or alerts representative of the actionable problem in the managed infrastructure. A situation room includes a collaborative interface (UI) for decomposing events from managed infrastructures. In response to production of the clusters one or more physical changes in a managed infrastructure hardware is made, where the hardware supports the flow and processing of information.
Abstract:
An event clustering system and associated methods include a feedback signalizer functor that responds to user interactions with already formed situations. The system and method then learns how to replicate the same situation when new alerts reoccur, or, creates similar situations. The feedback signalizer functor is a supervised machine learning approach to train a signalizer functor to reproduce a situation at varying degrees of precision.