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, computer system, and a computer program for data selection is provided. The present invention may include generating a first model associated with a dataset. The present invention may further include determining a first model performance level associated with the first model based on a plurality of dataset metric values of the dataset. The present invention may further include a plurality of data subsets of a dataset based on the first model performance level failing to exceed a performance threshold and calculating a plurality of subset metric values associated with the plurality of data subsets. The present invention may further include generating a second model associated with at least one data subset based on the plurality of subset metric values and determining an optimization associated with the first model based on a second model performance level associated with the second model exceeding the performance threshold.

Abstract: An approach for end-to-end anomaly detection and anomalous patterns identification is disclosed. The approach leverages the use of a GMM-LASSO (a selection operator-type, Lasso-type, generalized method of moments (GMM) estimator) algorithm and proposes a feedback loop where the window (i.e., anomalous window) is detected and then it is used to detect the anomalous patterns. For example, the approach can classify one or more sequential data; generates one or more vectors based on the one or more sequential data; clusters the one or more vectors into one or more clusters; determines a membership of the one or more vectors associated with the one or more clusters; updates the one or more clusters; and optimizes the one or more clusters with respect to a predefined threshold.

Abstract: A method, a computer program product, and a computer system handle a data gap in sequential data. The method includes receiving the sequential data for a period of time. The method includes selecting the data gap in the sequential data at a timestamp. The method includes determining a sliding window associated with the data gap based on the timestamp for a duration of time. The sliding window includes dependent data from which the data gap depends. The method includes, as a result of the dependent data of the sliding window including at least one window data gap, generating extracted patterns based on the dependent data to mask the at least one window data gap. The method includes determining a prediction to fill the data gap using a prediction model that takes as input modified data based on the dependent data and the extracted patterns.

Abstract: Localizing a faulty microservice in a microservice architecture is achieved by developing healthy execution sequence data for comparison to execution sequences during system failures. Oftentimes the faulty microservice does not emit a failure signal. Frequent sub-sequences arising from log template time series data during healthy execution facilitates localization of faulty services when there is no failure signal from the faulty service.

Abstract: Techniques that facilitate feedback loop learning between artificial intelligence systems are provided. In one example, a system includes a monitoring component and a machine learning component. The monitoring component identifies a data pattern associated with data for an artificial intelligence system. The machine learning component compares the data pattern to historical data patterns for the artificial intelligence system to facilitate modification of at least a component of the artificial intelligence system and/or one or more dependent systems of the artificial intelligence system.

Abstract: Embodiments relate to a computer platform to support processing of informational logs and corresponding performance data to detect and mitigate occurrence of anomalous behavior. Metrics are extracted from the informational logs and correlated with performance data, and in an exemplary embodiment golden signal metrics. A window or block of the logs is classified as potential candidates or indicators of anomalous behavior, which in an embodiment is indicative of potential failure or service outage. A control signal is dynamically issued to an operatively coupled device associated with the window or block of logs. The control signal is configured to selectively control a state of a physical device or process controlled by software, with the control directed at mitigating or eliminating the effect(s) of the anomalous behavior.

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: In an approach to risk prediction for bug-introducing changes, a computer retrieves one or more historic pull requests. A computer determines a unique file linking for each file included in the historic pull requests. A computer generates a file risk dataset. A computer performs chronological partitioning on the file risk dataset. A computer determines bug-introducing changes in the file risk dataset. A computer computes a collaborative file association between two or more of the files in the file risk dataset. A computer labels each of the files in the file risk dataset with an associated risk of introducing a bug. A computer generates a labelled file risk inducing ground truth dataset. A computer inputs the labelled file risk inducing ground truth dataset to a file risk prediction model. A computer extracts pull request features from the historic pull requests. A computer generates a pull request risk prediction model.

Abstract: Embodiments of the present invention provide computer-implemented methods, computer program products and systems. Embodiments of the present invention can receive position and location information. Embodiments of the present invention can generate a risk score for one or more maneuvers associated with a predicted trajectory of a vehicle. Embodiments of the present invention can generate a visual representation for each of the one or more maneuvers associated with the predicted trajectory of the vehicle based on the generated risk score associated with each maneuver. Embodiments of the present invention can integrate the generated visual representation into a user display.

Abstract: In an approach to risk prediction for bug-introducing changes, a computer retrieves one or more historic pull requests. A computer determines a unique file linking for each file included in the historic pull requests. A computer generates a file risk dataset. A computer performs chronological partitioning on the file risk dataset. A computer determines bug-introducing changes in the file risk dataset. A computer computes a collaborative file association between two or more of the files in the file risk dataset. A computer labels each of the files in the file risk dataset with an associated risk of introducing a bug. A computer generates a labelled file risk inducing ground truth dataset. A computer inputs the labelled file risk inducing ground truth dataset to a file risk prediction model. A computer extracts pull request features from the historic pull requests. A computer generates a pull request risk prediction model.

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: One or more systems, computer-implemented methods and/or computer program products to facilitate a process to transform original operational data into updated operational data. 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 transformation component that can transform original operational data of a first architecture into updated operational data employable at a second architectures, wherein the second architectures is an updated architectures relative to the first architecture. In one or more embodiments, the transformation component further can employ machine learning to match one or more data elements of the original operational data to one or more aspects of the second architecture.

Abstract: A system and associated method may utilize a processor that receives recall product instance group (RPIG) information (RPIGI) identifying a set of instances of products having a product type identifier (PTI) that are a subject of a product recall. The processor receives, from an imaging device associated with a user, an image containing a product instance. The method further comprises applying a classifier and determining a PTI. Based on the image, secondary product information is also identified. These may be compared with the RPIGI to determine a product instance membership. Information related to the product instance membership in the RPIG may be displayed.

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: Systems, computer-implemented methods, and computer program products that facilitate synthetic system fault generation are provided. According to an embodiment, a system can comprise a processor that executes the following computer-executable components stored in a non-transitory computer readable medium: a generator component that employs a trained artificial intelligence (AI) model to generate a synthetic system fault, represented as a combination of discrete parameters and continuous parameters that define a system state; and a fault assembler component that analyzes the synthetic system fault and generates textual content corresponding to the synthetic system fault.

Abstract: An approach for generating actionable explanations of change request classifications may be presented. A model may generate features associated with a change request may be disclosed. The model may be trained with historical change requests that have been labeled risky or not risky. The change request may be classified as risky or not risky. Candidate historical change requests with the same classification as the change request and occupying similar feature space as the change request may be identified from a historical change request repository. One or more features which had the most significant impact on the classification may be identified. A candidate historical change request with at least one significant feature impacting classification may be identified.

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.