Abstract: Feedback data corresponding to a historical system outage is received. One or more computer services are identified based on the feedback data. A weight for at least one of the one or more computer services is generated where the at least one of the one or more computer services is associated with an incident corresponding to the feedback data. An outage risk detection model is adjusted based on the weight generated for the at least one of the one or more computer services. A system outage is identified using the outage risk detection model.

Abstract: An outage risk detection alert is generated for one or more organizations of a plurality of organizations. Computer service incidents are monitored for the plurality of organizations to identify computer services having current computer service incidents. A count of organizations of the plurality of organizations that utilize a particular computer service and that have a current computer service incident related to the particular computer service within a plurality of time windows are aggregated to generate an aggregate count for the particular computer service for each time window of the plurality of time windows. An outage risk detection alert for the particular computer service is generated responsive to the aggregated count for a time window of the plurality of time windows surpassing a threshold level.

Abstract: Embodiments are directed towards identifying and decreasing operational pain and increasing system efficiency through health monitoring and management. This may be accomplished through measuring, monitoring, reducing meaningful incident behavior across an organization and using it to inform necessary changes in the organizational operations to improve efficiency, or the like. Ergonomic data or metrics collected by a resource management engine may be used to intelligently inform management decisions to increase human well-being across the organization's workforce to optimize overall system performance. Accordingly, resource management engines may identify areas in organizations that need improvement or repair. In some embodiments, resource management engines may be arranged to participate in a continuous feedback loop that may provide continuous overall system optimization.

Abstract: Embodiments are directed to managing operations. If Operations events are provided, event clusters may be associated with one or more Operations events, such that the Operations events may be associated with the event clusters based on characteristics of the Operations events. Metrics including resolution metrics, root cause analysis, notes, and other remediation information may be associated with the event clusters. Then a modeling engine may be employed to train models based on the Operations events, the event clusters, and the resolution metrics, such that the trained model may be trained to correlate and predict the resolution metrics from real-time Operations events. If real-time Operations events may be provided, the trained models may be employed to predict the resolution metrics that are associated with the real-time Operations events. If model performance degrades beyond accuracy requirements, new observations may be added to the training set and the model re-trained.

Abstract: Embodiments are directed to managing operations. If Operations events are provided, event clusters may be associated with one or more Operations events, such that the Operations events may be associated with the event clusters based on characteristics of the Operations events. Metrics including resolution metrics, root cause analysis, notes, and other remediation information may be associated with the event clusters. Then a modeling engine may be employed to train models based on the Operations events, the event clusters, and the resolution metrics, such that the trained model may be trained to correlate and predict the resolution metrics from real-time Operations events. If real-time Operations events may be provided, the trained models may be employed to predict the resolution metrics that are associated with the real-time Operations events. If model performance degrades beyond accuracy requirements, new observations may be added to the training set and the model re-trained.

Abstract: Implementations of this disclosure are directed towards the computer-implemented determination of interrupt events from a plurality of notification events generated by one or more of a plurality of computer systems. Interrupt events are associated with notifications designed to immediately alert a responder. A health score is generated as a weighted sum of one or more health sub-scores that are generated using one or more health sub-score models that take as input more health metrics representative of data associated with one or more interrupt events. The health sub-scores are representative of an impact relating to the management of operations of the plurality of computer systems and the weights used in the weighted score are representative of an estimation of the relative impact of the generated health sub-scores. The health sub-score models may be automatically modified according to an automated analysis of historic operational outcome metrics and historic scores.

Abstract: Embodiments are directed towards to managing operations for an organization. An operation engine may be employed to provide operations metrics based on events and resolution actions associated with an incident. Update information may be provided to console modules included in operations command consoles (OCCs) each console module may include module views. The console modules may be employed to provide data objects to the module views. The module views may be employed to provide different visualizations of the incident based on a role that may be associated with the console modules. An OCC may be employed to provide other resolution actions to the operations engine and then that operations engine may provide additional update information to the console modules based on the other resolution actions.

Abstract: Embodiments are directed towards embodiments for monitoring or predicting user health with respect their use of connected computers, such as, mobile phones, tablets, or the like. Activities associated with a user and a computing device may be monitored to determine activity events. One or more sub-scores may be provided in real time based on metrics provided as input to sub-score models such that the metrics are associated with the activity events. A health score associated with a probability of an occurrence of adverse user outcomes may be provided based on a health model that uses the sub-scores. An analysis engine may compare the health score to other health scores to predict in real time the adverse outcomes. The analysis engine may recommend one or more actions to decrease the probability of the occurrence of the adverse outcomes based on the result.

Abstract: Embodiments are directed towards identifying and decreasing operational pain and increasing system efficiency through health monitoring and management. This may be accomplished through measuring, monitoring, reducing meaningful incident behavior across an organization and using it to inform necessary changes in the organizational operations to improve efficiency, or the like. Ergonomic data or metrics collected by a resource management engine may be used to intelligently inform management decisions to increase human well-being across the organization's workforce to optimize overall system performance. Accordingly, resource management engines may identify areas in organizations that need improvement or repair. In some embodiments, resource management engines may be arranged to participate in a continuous feedback loop that may provide continuous overall system optimization.

Abstract: Embodiments are directed towards embodiments for monitoring or predicting user health with respect their use of connected computers, such as, mobile phones, tablets, or the like. Activities associated with a user and a computing device may be monitored to determine activity events. One or more sub-scores may be provided in real time based on metrics provided as input to sub-score models such that the metrics are associated with the activity events. A health score associated with a probability of an occurrence of adverse user outcomes may be provided based on a health model that uses the sub-scores. An analysis engine may compare the health score to other health scores to predict in real time the adverse outcomes. The analysis engine may recommend one or more actions to decrease the probability of the occurrence of the adverse outcomes based on the result.

Abstract: Embodiments are directed to managing operations. If Operations events are provided, event clusters may be associated with one or more Operations events, such that the Operations events may be associated with the event clusters based on characteristics of the Operations events. Metrics including resolution metrics, root cause analysis, notes, and other remediation information may be associated with the event clusters. Then a modeling engine may be employed to train models based on the Operations events, the event clusters, and the resolution metrics, such that the trained model may be trained to correlate and predict the resolution metrics from real-time Operations events. If real-time Operations events may be provided, the trained models may be employed to predict the resolution metrics that are associated with the real-time Operations events. If model performance degrades beyond accuracy requirements, new observations may be added to the training set and the model re-trained.

Abstract: Embodiments are directed towards to managing operations for an organization. An operation engine may be employed to provide operations metrics based on events and resolution actions associated with an incident. Update information may be provided to console modules included in operations command consoles (OCCs) each console module may include module views. The console modules may be employed to provide data objects to the module views. The module views may be employed to provide different visualizations of the incident based on a role that may be associated with the console modules. An OCC may be employed to provide other resolution actions to the operations engine and then that operations engine may provide additional update information to the console modules based on the other resolution actions.

Abstract: Embodiments are directed to managing operations. If Operations events are provided, event clusters may be associated with one or more Operations events, such that the Operations events may be associated with the event clusters based on characteristics of the Operations events. Metrics including resolution metrics, root cause analysis, notes, and other remediation information may be associated with the event clusters. Then a modeling engine may be employed to train models based on the Operations events, the event clusters, and the resolution metrics, such that the trained model may be trained to correlate and predict the resolution metrics from real-time Operations events. If real-time Operations events may be provided, the trained models may be employed to predict the resolution metrics that are associated with the real-time Operations events. If model performance degrades beyond accuracy requirements, new observations may be added to the training set and the model re-trained.

Abstract: Embodiments are directed to managing operations. If Operations events are provided, event clusters may be associated with one or more Operations events, such that the Operations events may be associated with the event clusters based on characteristics of the Operations events. Metrics including resolution metrics, root cause analysis, notes, and other remediation information may be associated with the event clusters. Then a modeling engine may be employed to train models based on the Operations events, the event clusters, and the resolution metrics, such that the trained model may be trained to correlate and predict the resolution metrics from real-time Operations events. If real-time Operations events may be provided, the trained models may be employed to predict the resolution metrics that are associated with the real-time Operations events. If model performance degrades beyond accuracy requirements, new observations may be added to the training set and the model re-trained.