Abstract: A turbomachinery engine can include a fan assembly with a plurality of variable pitch fan blades. The fan blades are configured such that they define a first VPF parameter and a second VPF parameter. The first VPF parameter is within a range of 0.10 to 0.40 and is defined as the hub-to-tip radius ratio divided by the fan pressure ratio. The second VPF parameter is within a range of 1-30 lbf/in2 and is defined as the bearing spanwise force divided by the fan area. In certain examples, the turbomachinery engine further includes a pitch change mechanism, a vane assembly, a core engine, and a gearbox.

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: An approach for training a recurrent neural network to create a model for anomaly detection in the topology of a network is disclosed. The approach comprises, creating an embedding vector for each resource in the network based on applying an embedding algorithm to each resource of the network. A feature vector is then created for each change to a resource in the network based on one or more properties of the change. A recurrent neural network can thus be trained with the embedding vectors and the feature vectors to create a model for anomaly detection in the topology of the network.

Abstract: Topology information including a plurality of snapshots of a network topology associated with respective points in time for a network can be received by an apparatus. Each snapshot is represented as a graph of nodes each corresponding to a network resource and having a node identifier. The graph for each snapshot is modified by replacing nodes representing network resources having the same role in the network with a single aggregated node. Feature learning is performed based on the modified graphs representing the plurality of snapshots, and determines a feature representation for each node in the modified graphs. An identifier for each node in the plurality of snapshots is associated with the corresponding feature representation for use in the correlation of network resources. Node identifiers for nodes in the same aggregated node in a modified graph are associated with the same feature representation.

Abstract: Aspects of the present invention disclose a method for automated incident prioritization in network monitoring systems. The method includes one or more processors determining historical sentiment impact scores for one or more incident parameters based at least in part on textual content of customer feedback and changes of a customer sentiment during a time period corresponding to one or more system incidents of the time period. The method further includes assigning a classification to the one or more system incidents, wherein the classification corresponds to the one or more incident parameters. The method further includes determining an active incident sentiment impact score for each of one or more active incidents. The method further includes applying the active incident sentiment impact scores as a factor to prioritize incident handling of the one or more active incidents.

Abstract: Topology information including a plurality of snapshots of a network topology associated with respective points in time for a network can be received by an apparatus. Each snapshot is represented as a graph of nodes each corresponding to a network resource and having a node identifier. The graph for each snapshot is modified by replacing nodes representing network resources having the same role in the network with a single aggregated node. Feature learning is performed based on the modified graphs representing the plurality of snapshots, and determines a feature representation for each node in the modified graphs. An identifier for each node in the plurality of snapshots is associated with the corresponding feature representation for use in the correlation of network resources. Node identifiers for nodes in the same aggregated node in a modified graph are associated with the same feature representation.

Abstract: A method and system are provided for retraining an analytic model. The method includes building, by a processor, a Markov chain for the analytic model. The Markov chain has only two states that consist of an alarm state and a no alarm state. The method further includes updating, by the processor, the Markov chain with observed states, for each of a plurality of timestamps evaluated during a burn-in period. The method also includes updating, by the processor, state transition probabilities within the Markov chain, for each of a plurality of timestamps evaluated after the burn-in period. The method additionally includes generating, by the processor, a signal for causing the model to be retrained, responsive to any of the state transition probabilities representing a probability of greater than 0.5 of seeing the alarm state in a previous interval and again in a current interval.

Abstract: Mechanisms for anomaly detection in a network management system are provided. The mechanisms collect metric data from a plurality of network devices and determine metric types for the metric data using metric type reference data. The mechanisms determine and apply properties from the metric type reference data to metrics of the determined metric types. The mechanisms monitor subsequent metric data for anomalies that do not conform to the applied properties.

Abstract: Deploying a model for anomaly detection in time series data. A period of data is received. A model of the period of data is received. It is determined that the model fits a part of the period of data and that the fitted part of the period of data includes the most recent data. A reduced model for the part of the period of data that fit the received model is built. The reduced model is deployed.

Abstract: Deploying a model for anomaly detection in time series data. A period of data is received. A model of the period of data is received. It is determined that the model fits a part of the period of data and that the fitted part of the period of data includes the most recent data. A reduced model for the part of the period of data that fit the received model is built. The reduced model is deployed.

Abstract: Mechanisms for anomaly detection in a network management system are provided. The mechanisms collect metric data from a plurality of network devices and determine metric types for the metric data using metric type reference data. The mechanisms determine and apply properties from the metric type reference data to metrics of the determined metric types. The mechanisms monitor subsequent metric data for anomalies that do not conform to the applied properties.

Abstract: Mechanisms for anomaly detection in a network management system are provided. The mechanisms collect metric data from a plurality of network devices and determine metric types for the metric data using metric type reference data. The mechanisms determine and apply properties from the metric type reference data to metrics of the determined metric types. The mechanisms monitor subsequent metric data for anomalies that do not conform to the applied properties.

Abstract: Method for monitoring data events using calendars are provided. Aspects include accessing a plurality of calendars, each calendar defining a schedule of calendar days and receiving a plurality of inputs from one or more applications, each input defining a data event for a specific source, for each calendar of the plurality of calendars. Aspects also include maintaining, for each data event source, a count for each calendar day and a count for each non-calendar day, for each calendar of the plurality of calendars. Aspects further include determining, for each data event source, if a comparison of the count for each calendar day and the count for each non-calendar day is statistically significant, and generating an output for a data event source, if the comparison of the count for each calendar day and the count for each non-calendar day is statistically significant.

Abstract: A method for producing silicon carbide substrates fit for epitaxial growth in a standard epitaxial chamber normally used for silicon wafers processing. Strict limitations are placed on any substrate that is to be processed in a chamber normally used for silicon substrates, so as to avoid contamination of the silicon wafers. To take full advantage of standard silicon processing equipment, the SiC substrates are of diameter of at least 150 mm. For proper growth of the SiC boule, the growth crucible is made to have interior volume that is six to twelve times the final growth volume of the boule. Also, the interior volume of the crucible is made to have height to width ratio of 0.8 to 4.0. Strict limits are placed on contamination, particles, and defects in each substrate.

Abstract: A method and system are provided for retraining an analytic model. The method includes building, by a processor, a Markov chain for the analytic model. The Markov chain has only two states that consist of an alarm state and a no alarm state. The method further includes updating, by the processor, the Markov chain with observed states, for each of a plurality of timestamps evaluated during a burn-in period. The method also includes updating, by the processor, state transition probabilities within the Markov chain, for each of a plurality of timestamps evaluated after the burn-in period. The method additionally includes generating, by the processor, a signal for causing the model to be retrained, responsive to any of the state transition probabilities representing a probability of greater than 0.5 of seeing the alarm state in a previous interval and again in a current interval.

Abstract: A computer implemented method comprises accessing a plurality of calendars, each calendar defining a schedule of calendar days, receiving a plurality of inputs from one or more applications, each input defining a data event for a specific source, for each calendar of the plurality of calendars, maintaining, for each data event source, a count for each calendar day and a count for each non-calendar day, for each calendar of the plurality of calendars, determining, for each data event source, if a comparison of the count for each calendar day and the count for each non-calendar day is statistically significant, and generating an output for a data event source, if the comparison of the count for each calendar day and the count for each non-calendar day is statistically significant.

Abstract: Deploying a model for anomaly detection in time series data. A period of data is received. A model of the period of data is received. It is determined that the model fits a part of the period of data and that the fitted part of the period of data includes the most recent data. A reduced model for the part of the period of data that fit the received model is built. The reduced model is deployed.

Abstract: A computer-implemented method of analyzing a plurality of metrics associated with one or more real-time streams of time-series data. For each metric, a set of time-series data for an interval of time is received, and one or more feature values determined from data of the stream prior to the interval of time are retrieved. One or more updated feature values are determined using the one or more retrieved feature values and the received set of time-series data, and the one or more updated feature values are stored in the store. The values of the metrics are then determined from the updated feature values, the plurality of metrics are then analyzed, for example to perform anomaly detection.

Abstract: A method for producing silicon carbide substrates fit for epitaxial growth in a standard epitaxial chamber normally used for silicon wafers processing. Strict limitations are placed on any substrate that is to be processed in a chamber normally used for silicon substrates, so as to avoid contamination of the silicon wafers. To take full advantage of standard silicon processing equipment, the SiC substrates are of diameter of at least 150 mm. For proper growth of the SiC boule, the growth crucible is made to have interior volume that is six to twelve times the final growth volume of the boule. Also, the interior volume of the crucible is made to have height to width ratio of 0.8 to 4.0. Strict limits are placed on contamination, particles, and defects in each substrate.