Abstract: Various embodiments herein described are directed to methods, apparatuses and computer program products configured for monitoring and improving the performance of complex network computer systems. In some examples, an example service health status monitoring server may generate service health status data objects based at least in part on system-monitored metric metadata of metric-based alert data object(s) and user-defined mapping metadata of metric-based rule data object(s). Additional examples provide various example user interfaces, such as, but not limited to, health status monitoring user interfaces, that facilitate various user inputs and software operations in a service health status monitoring system.

Abstract: Embodiments of the present disclosure provide improved identification and handling of root causes for data event(s). Some embodiments improve the accuracy of determinations of a root cause or likely order of root causes of a data event affecting any number of system(s), and cause transmission of data associated with such root cause(s) for use in triaging such data event(s) and/or facilitating efficient servicing to resolve the data event. Some embodiments utilize modified centrality algorithm(s) to efficiently and accurately identify a likely root cause of a data event in a computing environment. Some embodiments generate and/or output notifications that indicate the particular computing system(s) identified as a root cause of a data event, and/or the particular computing system(s) identified not as a root cause but affected by a data event of the root cause computing system.

Abstract: Embodiments of the present disclosure provide improved identification and handling of root causes for data event(s). Some embodiments improve the accuracy of determinations of a root cause or likely order of root causes of a data event affecting any number of system(s), and cause transmission of data associated with such root cause(s) for use in triaging such data event(s) and/or facilitating efficient servicing to resolve the data event. Some embodiments utilize modified centrality algorithm(s) to efficiently and accurately identify a likely root cause of a data event in a computing environment. Some embodiments generate and/or output notifications that indicate the particular computing system(s) identified as a root cause of a data event, and/or the particular computing system(s) identified not as a root cause but affected by a data event of the root cause computing system.

Abstract: A system and method including building propensity inclination dimensions of an affinity cluster, the propensity inclination dimensions maintained at a group profile level of the affinity cluster, identifying next best priority signals based on influencing events to determine a next best priority, applying a predictive algorithm to derive an influencing index, which is used to determine a tolerance level of the affinity cluster, deriving a duration of time of the tolerance level, and determining a critical mass of the affinity cluster required to achieve an objective of an organization.

Abstract: A method, system, and computer program product for implementing a digital conversion process is provided. The method includes receiving network data and text data associated with a text presentation being presented via a hardware device of a user. Semantic attributes of the text data are analyzed, and associated concepts and premises of the text data are determined. The network data is analyzed, and the user is associated with a plurality of users associated with the text presentation resulting in the formation of a group of users. A propensity inclination is determined for the group of users with respect to specialized digital dimension and retrieved images are associated with the text data. The retrieved images are presented to the user.

Abstract: A method, system, and computer program product for implementing a digital conversion process is provided. The method includes receiving network data and text data associated with a text presentation being presented via a hardware device of a user. Semantic attributes of the text data are analyzed, and associated concepts and premises of the text data are determined. The network data is analyzed, and the user is associated with a plurality of users associated with the text presentation resulting in the formation of a group of users. A propensity inclination is determined for the group of users with respect to specialized digital dimension and retrieved images are associated with the text data. The retrieved images are presented to the user.