Abstract: Described systems and techniques enable prediction of a state of an application at a future time, with high levels of accuracy and specificity. Accordingly, operators may be provided with sufficient warning to avert poor user experiences. Unsupervised machine learning techniques may be used to characterize current states of applications and underlying components in a standardized manner. The resulting data effectively provides labelled training data that may then be used by supervised machine learning algorithms to build state prediction models. Resulting state prediction models may then be deployed and used to predict an application state of an application at a specified future time.

Abstract: A method of a device-level management based on a digital experience includes developing a calculated device index (CDI) expression for a managed device of a managed network. The CDI expression includes a combination of weighted, normalized attribute values. The attributes reflect a digital experience metric of a user relative to the device. The method includes determining a normal device index range (NDIR) that defines values of a CDI indicative of normal operation of the device. The method includes monitoring current attribute data representative of multiple attributes associated with the device. Based on the current attribute data, the method includes computing the CDI using the CDI expression and evaluating the computed CDI relative to the NDIR. Responsive to the CDI being outside the NDIR, the method includes identifying a first attribute that is in an anomalous condition and that contributed to the CDI and implementing an action to mitigate the condition.

Abstract: Described systems and techniques enable prediction of a state of an application at a future time, with high levels of accuracy and specificity. Accordingly, operators may be provided with sufficient warning to avert poor user experiences. Unsupervised machine learning techniques may be used to characterize current states of applications and underlying components in a standardized manner. The resulting data effectively provides labelled training data that may then be used by supervised machine learning algorithms to build state prediction models. Resulting state prediction models may then be deployed and used to predict an application state of an application at a specified future time.

Abstract: A system, method, and computer program product for intelligent-skills-matching includes receiving a plurality of tickets, where each ticket in the plurality of tickets includes a plurality of fields and at least one agent who resolved the ticket is identified. A clustering algorithm is used on one or more of the plurality of fields to determine skills from the plurality of tickets. A taxonomy of the skills is generated using a taxonomy-construction algorithm. Using the taxonomy of the skills, a skills matrix or a skills knowledge graph is created with agents assigned to the skills.

Abstract: Information technology service management (ITSM) incident reports are converted from textual data to multiple vectors using an encoder and parameters are selected, where the parameters include a base cluster number and a threshold value. A base group of clusters is generated using an unsupervised machine learning clustering algorithm with the vectors and the parameters as input. A cluster quality score is computed for each of the base group of clusters. Each cluster from the base group of clusters with the cluster quality score above the threshold value is recursively split into new clusters until the cluster quality score for each cluster in the new clusters is below the threshold value. A final group of clusters is output, where each cluster from the final group of clusters represents ITSM incident reports related to a same problem.

Abstract: Described systems and techniques enable prediction of a state of an application at a future time, with high levels of accuracy and specificity. Accordingly, operators may be provided with sufficient warning to avert poor user experiences. Unsupervised machine learning techniques may be used to characterize current states of applications and underlying components in a standardized manner. The resulting data effectively provides labelled training data that may then be used by supervised machine learning algorithms to build state prediction models. Resulting state prediction models may then be deployed and used to predict an application state of an application at a specified future time.