Abstract: Systems, computer-implemented methods, and computer program products that can facilitate refinement of a predicted event based on explainability data 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 an interpreter component that identifies a probable cause of a predicted event based on explainability data. The computer executable components can further comprise an enrichment component that executes a diagnostic analysis based on the probable cause.

Abstract: Systems, computer-implemented methods, and computer program products that can facilitate refinement of a predicted event based on explainability data 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 an interpreter component that identifies a probable cause of a predicted event based on explainability data. The computer executable components can further comprise an enrichment component that executes a diagnostic analysis based on the probable cause.

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: Systems/techniques for generating training data via reinforcement learning fault-injection are provided. A system can access a computing application. In various aspects, the system can train one or more machine learning models based on responses of the computing application to iterative fault-injections determined via reinforcement learning. More specifically, the system can: inject a first fault into the computing application; record a resultant dataset outputted by the computing application in response to the first fault; train the one or more machine learning models on the resultant dataset and the first fault; compute a reinforcement learning reward based on performance metrics of the one or more machine learning models and based on a quantity of the resultant dataset; update, via execution of a reinforcement learning algorithm, the fault-injection policy based on the reinforcement learning reward; and inject a second fault into the computing application, based on the updated fault-injection policy.

Abstract: A computer-implemented method is provided that includes learning causal relationships between two or more application micro-services, and applying the learned causal relationships to dynamically localize an application fault. First micro-service error log data corresponding to selectively injected errors is collected. A learned causal graph is generated based on the collected first micro-service error log data. Second micro-service error log data corresponding to a detected application and an ancestral matrix is built using the learned causal graph and the second micro-service error log data. The ancestral matrix is leveraged to identify the source of the error, and the micro-service associated with the identified error source is also subject to identification. A computer system and a computer program product are also provided.

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: 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 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: Systems, computer-implemented methods, and computer program products that can facilitate refinement of a predicted event based on explainability data 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 an interpreter component that identifies a probable cause of a predicted event based on explainability data. The computer executable components can further comprise an enrichment component that executes a diagnostic analysis based on the probable cause.

Abstract: An approach is provided for aggregating business data. Based on received columns, attributes, and keys of a dataset, the dataset is registered in a central hub of datasets which include data that is independently and locally maintained. Other datasets in the central hub that include columns, attributes, or keys that match the received columns, attributes, or keys, respectively, are determined and displayed. Responsive to receiving a user selection of one or more of the displayed datasets, cross-links between the dataset and each of the one or more selected datasets are generated. Based on a modification of a selected one of the cross-links and a second user interacting with the central hub, the modification and other cross-link(s) are displayed.

Abstract: An approach is provided for aggregating business data. Based on received columns, attributes, and keys of a dataset, the dataset is registered in a central hub of datasets which include data that is independently and locally maintained. Other datasets in the central hub that include columns, attributes, or keys that match the received columns, attributes, or keys, respectively, are determined and displayed. Responsive to receiving a user selection of one or more of the displayed datasets, cross-links between the dataset and each of the one or more selected datasets are generated. Based on a modification of a selected one of the cross-links and a second user interacting with the central hub, the modification and other cross-link(s) are displayed.

Abstract: An approach is provided for aggregating business data. Based on received columns, attributes, and keys of a dataset, the dataset is registered in a central hub of datasets which include data that is independently and locally maintained. Other datasets in the central hub that include columns, attributes, or keys that match the received columns, attributes, or keys, respectively, are determined and displayed. Responsive to receiving a user selection of one or more of the displayed datasets, cross-links between the dataset and each of the one or more selected datasets are generated. Based on a modification of a selected one of the cross-links and a second user interacting with the central hub, the modification and other cross-link(s) are displayed. Based on the second user's selection of the modification or one of the other cross-link(s), an analysis of data in the dataset is performed.

Abstract: An approach is provided for aggregating business data. Based on received columns, attributes, and keys of a dataset, the dataset is registered in a central hub of datasets which include data that is independently and locally maintained. Other datasets in the central hub that include columns, attributes, or keys that match the received columns, attributes, or keys, respectively, are determined and displayed. Responsive to receiving a user selection of one or more of the displayed datasets, cross-links between the dataset and each of the one or more selected datasets are generated. Based on a modification of a selected one of the cross-links and a second user interacting with the central hub, the modification and other cross-link(s) are displayed. Based on the second user's selection of the modification or one of the other cross-link(s), an analysis of data in the dataset is performed.