Abstract: Techniques are provided for dynamic alert suppression policy management. In one embodiment, the techniques involve receiving an event stream, wherein the event stream includes metric values comprising at least one of: log anomaly data and metric anomaly data, determining an anomalous event based on the event stream, determining a persistent region of the anomalous event, determining a quantum representation of the persistent region, determining X-Y values of the persistent region based on the quantum representation, and generating a policy based on a set of the X-Y values.

Abstract: Techniques are provided for dynamic alert suppression policy management. In one embodiment, the techniques involve receiving an event stream, wherein the event stream includes metric values comprising at least one of: log anomaly data and metric anomaly data, determining an anomalous event based on the event stream, determining a persistent region of the anomalous event, determining a quantum representation of the persistent region, determining X-Y values of the persistent region based on the quantum representation, and generating a policy based on a set of the X-Y values.

Abstract: Computer implemented methods, systems, and computer program products include program code executing on a processor(s) that obtain temporal input from computing resources. The processor(s) extracts patterns from the temporal input, determine if relevant domain knowledge is available. If relevant data is available, the processor(s) utilizes it to identify patterns indicating anomalous workloads, label data in the patterns, and generate anomaly scores. If relevant data is not available, the processor(s) applies unsupervised anomaly detection algorithm(s) to identify patterns as indicative of anomalous workloads and label these data. The processor(s) generates anomaly scores for the labeled data and unlabeled data. The processor(s) determine which patterns indicates anomalous data or normal data by comparing relevant anomaly scores to a pre-defined threshold.

Abstract: A computer-implemented method and a computer system for identifying persistent anomalies for failure prediction. The computer system receives a time series data stream. The computer system received a predetermined number N and a predetermined number M which is a fraction of N. The computer system segments the time series data stream into N consecutive sliding windows. The computer system performs supervised persistent anomaly detection to determine whether anomalies across the N consecutive sliding windows are persistent, by using a binary classification model. The computer system performs unsupervised persistent anomaly detection to determine whether the anomalies across the N consecutive sliding windows are persistent. The computer system combines results of the supervised persistent anomaly detection and results of the unsupervised persistent anomaly detection to determine persistent anomalies.

Abstract: An embodiment includes detecting a set of anomalies recorded during a first predefined window of time in log entries for a computer environment. The embodiment also includes generating cluster data representative of a cluster of anomalies from among the set of anomalies, where the cluster is formed using a lattice clustering algorithm that spatially distinguishes the cluster of anomalies from other anomalies in the set of anomalies. The embodiment also includes composing an explanation using log templates generated from log entries associated with the cluster of anomalies.

Abstract: Detecting system log anomalies by receiving multivariate time-series system log data an multivariate metric data, inferring system metrics from the system log data and metric data, receiving a metric causal graph including causal relationships between system metrics, determining a univariate variation score for the system metrics, determining a causal variation score for the multivariate time series system metric data, according to the causal graph, determining an overall activity score according to the univariate variation score, and causal variation score, and altering a review window duration according to the activity score.

Abstract: An embodiment includes detecting a set of anomalies recorded during a first predefined window of time in log entries for a computer environment. The embodiment also includes generating cluster data representative of a cluster of anomalies from among the set of anomalies, where the cluster is formed using a lattice clustering algorithm that spatially distinguishes the cluster of anomalies from other anomalies in the set of anomalies. The embodiment also includes composing an explanation using log templates generated from log entries associated with the cluster of anomalies.

Abstract: Detecting system log anomalies by receiving multivariate time-series system log data an multivariate metric data, inferring system metrics from the system log data and metric data, receiving a metric causal graph including causal relationships between system metrics, determining a univariate variation score for the system metrics, determining a causal variation score for the multivariate time series system metric data, according to the causal graph, determining an overall activity score according to the univariate variation score, and causal variation score, and altering a review window duration according to the activity score.

Abstract: A method, computer system, and a computer program product for providing gratuity recommendations is provided. The present invention may include receiving a service request from a user, wherein a service is to be provided according to the service request by a service provider. The present invention may include accessing data for the service request. The present invention may include determining a gratuity for the service request based on the data accessed for the service request. The present invention may include processing the gratuity according to preferences of the service provider.

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: Embodiments relate to an intelligent computer platform to multi-dimensionally optimize device operation. Static hardware device data are acquired and dynamic hardware characteristic data are tracked over one or more temporal segments. A neural model (NM) is trained with corresponding device and network data. The acquired static and dynamic data are input into the NM, and locale processing patterns corresponding to the inputted data are identified. One or more data points and corresponding measurements of the tracked dynamic hardware characteristic data are temporally analyzed. A processing locale corresponding to the temporal analysis is identified and returned as output data, and one or more encoded actions in compliance with the identified processing locale are selectively implemented.

Abstract: Embodiments relate to an intelligent computer platform to multi-dimensionally optimize device operation. Static hardware device data is acquired and dynamic hardware characteristic data is tracked over one or more temporal segments. A neural model (NM) is trained with corresponding device and network data. The acquired static and dynamic data is populated into the MLM, and locale processing patterns corresponding to the populated data are identified. One or more data points and corresponding measurements of the tracked hardware device characteristic data are temporally analyzed. A processing locale corresponding to the temporal analysis is identified and returned as output data, and one or more encoded actions in compliance with the identified processing locale are selectively implemented.

Abstract: A component of an environment having available bandwidth for performing a task is located. Authorization to connect a device associated with the task to the component is granted. In response to determining that a set of one or more conditions are met, the device is connected to the component. The connection provides network connectivity to the device via the component.

Abstract: Embodiments of the present invention provide methods for generating an augmented reality experience based on Knowledge Media. One method can include receiving one or more Knowledge Media. Transforming the one or more Knowledge Media into consumable steps. Extracting hardware information from the one or more Knowledge Media. Generating a three-dimensional point cloud model of the hardware based on the one or more Knowledge Media, and outputting an augmented reality experience based on an annotated three-dimensional point cloud model.

Abstract: A component of an environment having available bandwidth for performing a task is located. Authorization to connect a device associated with the task to the component is granted. In response to determining that a set of one or more conditions are met, the device is connected to the component. The connection provides network connectivity to the device via the component.

Abstract: A component of an environment having available bandwidth for performing a task is located. Authorization to connect a device associated with the task to the component is granted. In response to determining that a set of one or more conditions are met, the device is connected to the component. The connection provides network connectivity to the device via the component.

Abstract: Embodiments of the present invention provide methods for generating an augmented reality experience based on Knowledge Media. One method can include receiving one or more Knowledge Media. Transforming the one or more Knowledge Media into consumable steps. Extracting hardware information from the one or more Knowledge Media. Generating a three-dimensional point cloud model of the hardware based on the one or more Knowledge Media, and outputting an augmented reality experience based on an annotated three-dimensional point cloud model.

Abstract: A component of an environment having available bandwidth for performing a task is located. Authorization to connect a device associated with the task to the component is granted. In response to determining that a set of one or more conditions are met, the device is connected to the component. The connection provides network connectivity to the device via the component.