Abstract: A method, a computer system, and a computer program product for mapping operational records to a topology graph. Embodiments of the present invention may include generating an event frequent pattern using operational records. Embodiments of the present invention may include integrating topology-based event frequent patterns. Embodiments of the present invention may include mapping the operational records with an embedding engine. Embodiments of the present invention may include predicting incident events. Embodiments of the present invention may include receiving labeled patterns to the embedding engine for an active learning cycle.

Abstract: In an approach to linking operational data with issues, a new event is received. The new event is associated to a story, where the story is related to an identified problem within the system, and further where the new event is associated with the story using machine learning techniques. The story is associated to related change requests based on a similarity between the story and related change requests, where the similarity between the story and the related change requests is associated using the machine learning techniques. A cost is calculated for the story. Responsive to associating the new event with a specific change request, the priority of the specific change request is updated based on the cost for the story.

Abstract: Computerized interactive feature visualization is carried out on a data set—a plurality of insight classes rank a plurality of features of the data set. Via a computerized user interface, user feedback is obtained based on the interactive feature visualization—a user selects and ranks a subset of the features. At least one transformation function is applied to at least one feature of the subset of features selected by the user, to automatically construct, with a computer, at least one additional feature for the data set. The data set with the at least one additional feature is a transformed data set. In some cases, a supervised task is carried out on the final data set; accuracy of a machine learning system implementing the at least one supervised task can be enhanced by the at least one additional feature, and/or a physical system can be controlled based on results of the at least one supervised task.

Abstract: In an approach to linking operational data with issues, a new event is received. The new event is associated to a story, where the story is related to an identified problem within the system, and further where the new event is associated with the story using machine learning techniques. The story is associated to related change requests based on a similarity between the story and related change requests, where the similarity between the story and the related change requests is associated using the machine learning techniques. A cost is calculated for the story. Responsive to associating the new event with a specific change request, the priority of the specific change request is updated based on the cost for the story.

Abstract: An approach to recommending corrective action to computing system event errors. The approach may include generating a textual description of an event error. The approach may include transforming the textual description into feature vectors with a domain-specific word embedding module. The approach may also include generating a recommendation to correct the event error based on an analysis of the feature vectors. Additionally, the recommendation may be presented for verification.

Abstract: Embodiments of the present invention provide methods, computer program products, and systems. Embodiments of the present invention can dynamically determine one or more endpoints to fulfill a user request. Embodiments of the present invention can select the dynamically determined one or more endpoints as the one or more endpoints that fulfill the user request. Embodiments of the present invention can execute the selected one or more endpoints to fulfill the user request.

Abstract: A method, a computer system, and a computer program product for a shiftleft topology construction is provided. Embodiments of the present invention may include collecting datasets. Embodiments of the present invention may include extracting topological entities from the datasets. Embodiments of the present invention may include correlating a plurality of data from the topological entities. Embodiments of the present invention may include mapping the topological entities. Embodiments of the present invention may include marking entry points for a plurality of subgraphs of the topological entities. Embodiments of the present invention may include constructing a topology graph.

Abstract: A computer implemented method for identifying an application topology includes identifying a sandbox environment corresponding to an application of interest, analyzing the sandbox environment to identify a set of communication links between services within the sandbox environment indicating a first topology, identifying a production system corresponding to the application of interest, querying the production system to identify a set of structural dependencies indicating a second topology, and creating a complete topology of the cloud application by combining the first topology and the second topology. A computer program product and computer system for identifying an application topology are additionally disclosed herein.

Abstract: An approach to recommending corrective action to computing system event errors. The approach may include generating a textual description of an event error. The approach may include transforming the textual description into feature vectors with a domain-specific word embedding module. The approach may also include generating a recommendation to correct the event error based on an analysis of the feature vectors. Additionally, the recommendation may be presented for verification.

Abstract: A system, computer program product, and method are provided for orchestrating a multi objective optimization of an application. A set of two or more key performance indicators (KPIs) and one or more parameters associated with the application are received. A machine learning (ML) based surrogate function learning model in combination with an acquisition function is leveraged to conduct one or more adaptive trials. Each trial consists of a specific configuration of the one or more parameters. A pareto surface of the KPIs of the application is computed based on the observations of KPI values from each adaptive trial. The pareto surface is explored and an optimal operating point is selected for the application. The application is then executed at the selected operating point.

Abstract: A computer-implemented method of cross-environment event correlation includes determining one or more correlated events about an issue across a plurality of domains. A knowledge data is extracted from the issue determined from the one or more correlated events is performed. A correlation graph is generated from the extracted knowledge to trace the issue and group the correlated events into one or more event groups to represent their relationship with the issue. A logical reasoning description is constructed based on the generated correlation graph for a domain-space exploration related to how the issue in one domain affects another domain of the plurality of domains. The one or more event groups of correlated events is provided with an explanation about a cause of the issue based on the logical reasoning description.

Abstract: A computer implemented method for identifying an application topology includes identifying a sandbox environment corresponding to an application of interest, analyzing the sandbox environment to identify a set of communication links between services within the sandbox environment indicating a first topology, identifying a production system corresponding to the application of interest, querying the production system to identify a set of structural dependencies indicating a second topology, and creating a complete topology of the cloud application by combining the first topology and the second topology. A computer program product and computer system for identifying an application topology are additionally disclosed herein.

Abstract: A method, a computer system, and a computer program product for mapping operational records to a topology graph. Embodiments of the present invention may include generating an event frequent pattern using operational records. Embodiments of the present invention may include integrating topology-based event frequent patterns. Embodiments of the present invention may include mapping the operational records with an embedding engine. Embodiments of the present invention may include predicting incident events. Embodiments of the present invention may include receiving labeled patterns to the embedding engine for an active learning cycle.

Abstract: A system, computer program product, and method are provided for orchestrating a multi objective optimization of an application. A set of two or more key performance indicators (KPIs) and one or more parameters associated with the application are received. A machine learning (ML) based surrogate function learning model in combination with an acquisition function is leveraged to conduct one or more adaptive trials. Each trial consists of a specific configuration of the one or more parameters. A pareto surface of the KPIs of the application is computed based on the observations of KPI values from each adaptive trial. The pareto surface is explored and an optimal operating point is selected for the application. The application is then executed at the selected operating point.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate fault localization and alert aggregation are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a graph component that employs an algorithm to generate a directed graph of computing elements having performance alerts in one or more abstraction layers of a computing environment. The computer executable components can further comprise a fault localization component that employs a topological sort algorithm to identify one or more of the computing elements causing the performance alerts based on the directed graph.

Abstract: Methods for repairing flange bolt holes and the resulting flange bolt holes are provided. The methods and products include the incorporation of a coating system comprising a corrosion resistant layer, which can be formed by resistance plug welding, slurry or sol-gel processing, or thermal/cold spray processing. The corrosion resistant layer can be a super alloy or ceramic material and is different than the base material of the flange bolt hole. Corrosion of the flange bolt hole can be reduced or prevented from occurring with the use of the coating system.

Abstract: A silicone bond coat composition having a viscosity of less than 1,600 centistokes is applied to substantially all external surfaces of the article and then cured. A liquid silicone elastomer outer coat composition comprising a high viscosity first liquid silicone elastomer formulation and a low viscosity second liquid silicone elastomer formulation is then applied and cured to provide a protected article having a complex shape. Optimal coatings result from a careful balancing of component viscosities. In an embodiment, the first formulation has a viscosity greater than 300,000 centistokes, and the second formulation has a viscosity less than 6,000 centistokes, and the liquid silicone elastomer outer coat composition comprises from about 60 to about 40 percent by weight of the first formulation and from about 40 to about 60 percent by weight of the second liquid silicone elastomer formulation.

Abstract: A specification of a topology of a microservices application is obtained as a plurality of nodes corresponding to a plurality of microservices of the microservices application. The plurality of nodes includes a root node, each of the plurality of nodes other than the root node has a timeout parameter and a retries parameter. Via constrained black box optimization, optimized values are selected for the timeout parameter and the retries parameter for each of the plurality of nodes other than the root node, subject to satisfying a specified end-to-end latency for the microservices application and minimizing an error rate for the microservices application. The microservices application is configured in accordance with the optimized values. At least one external request to the root node is responded to with the microservices application configured in accordance with the optimized values.

Abstract: A specification of a topology of a microservices application is obtained as a plurality of nodes corresponding to a plurality of microservices of the microservices application. the plurality of nodes includes a root node, each of the plurality of nodes other than the root node has a timeout parameter and a retries parameter. Via constrained black box optimization, optimized values are selected for the timeout parameter and the retries parameter for each of the plurality of nodes other than the root node, subject to satisfying a specified end-to-end latency for the microservices application and minimizing an error rate for the microservices application. the microservices application is configured in accordance with the optimized values. At least one external request to the root node is responded to with the microservices application configured in accordance with the optimized values.

Abstract: An analysis system for a logic program for a programmable logic controller (PLC) is provided. The analysis system includes a non-transitory memory and a processor. The processor is configured to receive the logic program and divide it into a plurality of segments. The processor is configured to identify a plurality of features among the plurality of segments. The processor is configured to assign the plurality of features to respective classes. The processor is configured to map the plurality of features to respective data objects corresponding to the respective classes. The processor is configured to write the respective data objects to the non-transitory memory.