Abstract: Processing within a computing environment is facilitated by using a machine learning implicit-anomaly model to determine a possibility of an implicit anomaly within a system based on system metrics data obtained during running of one or more test cases on the system. The process further includes determining, using artificial intelligence, occurrence of an incident within the system associated with running of the one or more test cases. Determining the occurrence of the incident uses the determined possibility of the implicit anomaly within the system based on the system metrics data, and the process further includes initiating an action based on the occurrence of the incident within the system with running of the one or more test cases.

Abstract: Aspects of the invention include determining an event associated with a computing system, the event occurring at a first time, obtaining system data associated with the computing system, determining a system state of the computing system at the first time based on the system data, determining, based on the system state, two or more system data clusters comprising clustered system data associated with the system state of the computing system, determining, via an interpolation algorithm, an interpolated data value for the first time based on the system data, and adjusting the interpolated data value based on a determination that the interpolate data value is outside the two or more system data clusters.

Abstract: A computer-implemented method for determining influence of applications on system performance includes collecting, by a processor, for several applications that are executing on a computing system, respective instrumentation data during multiple time-segments. The method further includes determining, for each of the applications, a performance value and a robustness value for each of the time-segments based on the respective instrumentation data. Further, using the performance value and robustness value for each time-segment, multiple health-waveforms are generated, where a health-waveform is generated for each respective application. The method further includes determining, by the processor, an influence-factor of a first application on a second application, the first application and the second application are executing on the computing system.

Abstract: Aspects of the invention include determining an event associated with a computing system, the event occurring at a first time, obtaining system data associated with the computing system, determining a system state of the computing system at the first time based on the system data, determining, based on the system state, two or more system data clusters comprising clustered system data associated with the system state of the computing system, determining, via an interpolation algorithm, an interpolated data value for the first time based on the system data, and adjusting the interpolated data value based on a determination that the interpolate data value is outside the two or more system data clusters.

Abstract: A computer-implemented method for determining influence of applications on system performance includes collecting, by a processor, for several applications that are executing on a computing system, respective instrumentation data during multiple time-segments. The method further includes determining, for each of the applications, a performance value and a robustness value for each of the time-segments based on the respective instrumentation data. Further, using the performance value and robustness value for each time-segment, multiple health-waveforms are generated, where a health-waveform is generated for each respective application. The method further includes determining, by the processor, an influence-factor of a first application on a second application, the first application and the second application are executing on the computing system.

Abstract: Aspects of the invention include a computer-implemented method that includes converting runtime data points that are indicative of an influence of the runtime data points on a robustness and performance of a computing system. The runtime data points are clustered, wherein each cluster respectively represents a temporal state of the computing system. Each cluster is translated into a three-dimensional representation based on a probability density of the runtime data points of each cluster. A time-based vector is generated, where the vector describes a transition from a first three-dimensional representation to a second three-dimensional representation. Each three-dimensional representation traversed by the time-based vector represents a respective state of the computing system. The time-based vector is compared with a baseline vector. An anomaly alert is issued based at least in part on the comparison.

Abstract: A method for filtering out nodes associated with hierarchical data structures is provided. The method may include generating node controllers on the hierarchical data structures. The method may include determining whether user actions are received on a first node controller, a second node controller, and a third node controller associated with the generated plurality of node controllers. The method may include filtering node levels associated with hierarchical data structures, and presenting the hierarchical data structures without the filtered out node levels. The method may include filtering out sibling nodes associated with the hierarchical data structures, and presenting the hierarchical data structures without the filtered out sibling nodes. The may further include reverse-collapsing first node levels and filtering out second node levels based on the reverse-collapsed first node levels, and presenting the hierarchical data structures without the filtered out second node levels.

Abstract: A method for filtering out nodes associated with hierarchical data structures is provided. The method may include generating node controllers on the hierarchical data structures. The method may include determining whether user actions are received on a first node controller, a second node controller, and a third node controller associated with the generated plurality of node controllers. The method may include filtering node levels associated with hierarchical data structures, and presenting the hierarchical data structures without the filtered out node levels. The method may include filtering out sibling nodes associated with the hierarchical data structures, and presenting the hierarchical data structures without the filtered out sibling nodes. The may further include reverse-collapsing first node levels and filtering out second node levels based on the reverse-collapsed first node levels, and presenting the hierarchical data structures without the filtered out second node levels.

Abstract: A method for filtering out nodes associated with hierarchical data structures is provided. The method may include generating node controllers on the hierarchical data structures. The method may include determining whether user actions are received on a first node controller, a second node controller, and a third node controller associated with the generated plurality of node controllers. The method may include filtering node levels associated with hierarchical data structures, and presenting the hierarchical data structures without the filtered out node levels. The method may include filtering out sibling nodes associated with the hierarchical data structures, and presenting the hierarchical data structures without the filtered out sibling nodes. The may further include reverse-collapsing first node levels and filtering out second node levels based on the reverse-collapsed first node levels, and presenting the hierarchical data structures without the filtered out second node levels.

Abstract: A method for filtering out nodes associated with hierarchical data structures is provided. The method may include generating node controllers on the hierarchical data structures. The method may include determining whether user actions are received on a first node controller, a second node controller, and a third node controller associated with the generated plurality of node controllers. The method may include filtering node levels associated with hierarchical data structures, and presenting the hierarchical data structures without the filtered out node levels. The method may include filtering out sibling nodes associated with the hierarchical data structures, and presenting the hierarchical data structures without the filtered out sibling nodes. The may further include reverse-collapsing first node levels and filtering out second node levels based on the reverse-collapsed first node levels, and presenting the hierarchical data structures without the filtered out second node levels.