Abstract: Methods, apparatuses, or computer program products disclosed herein provide for receiving an API object via a communication channel, applying a machine learning model to (i) the API object and (ii) one or more graph embedding structures for a portion of graph data to generate one or more predictive inferences related to the API object, and initiating a resolution action related to the API object based at least in part on the one or more predictive inferences.

Abstract: Apparatus, methods, and computer program products for outputting a technology operations incident event response interface component for rendering to a technology operations incident event response interface. A method may include identifying a technology operations incident event, filtering a plurality of development resource reference unit embedding vectors, generating a candidate development resource reference unit structure, generating a candidate technology operations incident responsive direction, generating a technology operations incident event response interface component, and outputting the technology operations incident event response interface component.

Abstract: Apparatus, methods, and computer program products for outputting a technology operations incident event response interface component for rendering to a technology operations incident event response interface. A method may include identifying a technology operations incident event, filtering a plurality of development resource reference unit embedding vectors, generating a candidate development resource reference unit structure, generating a candidate technology operations incident responsive direction, generating a technology operations incident event response interface component, and outputting the technology operations incident event response interface component.

Abstract: Apparatus, methods, and computer program products for outputting a technology operations incident event response interface component for rendering to a technology operations incident event response interface. A method may include identifying a technology operations incident event, filtering a plurality of development resource reference unit embedding vectors, generating a candidate development resource reference unit structure, generating a candidate technology operations incident responsive direction, generating a technology operations incident event response interface component, and outputting the technology operations incident event response interface component.

Abstract: Apparatus, methods, and computer program products for outputting a technology operations incident event response interface component for rendering to a technology operations incident event response interface. A method may include identifying a technology operations incident event, filtering a plurality of development resource reference unit embedding vectors, generating a candidate development resource reference unit structure, generating a candidate technology operations incident responsive direction, generating a technology operations incident event response interface component, and outputting the technology operations incident event response interface component.

Abstract: Automated system for predicting software application framework deployment changes for incident mitigation is provided. An incident indication for an incident may be received. One or more incident features associated with the incident may be extracted. One or more data objects associated with the incident may be identified based on the one or more incident features. Candidate incident mitigation dataset comprising a plurality of incident mitigating predictions may be generated based on relation data and a topology graph structure associated with the one or more data objects. A ranked candidate incident mitigation dataset may be generated using one or more ranking models. One or more incident mitigating predictions from the ranked candidate incident mitigation dataset may be selected based on a rank value associated with each of the one or more incident mitigating predictions.

Abstract: Automated system for predicting software application incident-causing deployments using a ranking framework is provided. A plurality of candidate code deployment data objects for an incident comprising an affected service data object may be identified. For each candidate code deployment data object of the plurality of candidate code deployment data objects a semantic similarity score, a topological distance score, and a temporal score may be generated. The plurality of candidate code deployment data objects may be ranked using a ranking model and based on the semantic similarity score, the topological distance score, and the temporal score for each candidate code deployment data object.

Abstract: Various embodiments disclosed herein are directed to a system, method, apparatus, and/or a computer program product that are configured to embody an alert management system that is adapted to efficiently analyze and cluster different types of alerts (e.g., heterogenous alerts) including system generated alerts and user generated alerts. In one example, system generated alerts are analyzed using one or more tag based clustering algorithms while user generated alerts are analyzed using one or more machine learning clustering models. In another example, the alert management system may be configured to improve alert clustering efficiency by using tag based clustering algorithms on alerts that satisfy a tag presence threshold while machine-learning based clustering models are used on alerts that do not satisfy the tag presence threshold. In various embodiments, confidence metrics are calculated and used to reduce the number of false positive alert clusters and to improve alert cluster quality.

Abstract: Various embodiments disclosed herein are directed to a system, method, apparatus, and/or a computer program product that are configured to programmatically analyze alert clusters and make recommendations to alert managers as to alert policy changes that might reduce alert volume without hindering the intended performance of the alert management system. For example, various embodiments are configured to programmatically determine if a threshold number of alerts within an alert cluster are insignificant and output an alert policy change recommendation interface that is configured to allow alert mangers to execute adjustments to underlying alert policies. In some embodiments, alert policy changes are recommended only when a threshold number of alerts within an alert cluster are insignificant and such threshold number of alerts are determined to be increasing within the monitored software application framework.

Abstract: Various embodiments disclosed herein are directed to a system, method, apparatus, and/or a computer program product that are configured to create an alert cluster list interface for viewing alert clusters (rather than individual alerts) in an alert management system. The alert clusters presented via the alert cluster list interface are programmatically classified as similar or related using an alert clustering model as discussed herein. By providing the alert cluster list interface, various embodiments provide an automated and consolidated view of the alert landscape presented at any given time within a software application framework.

Abstract: Apparatus, methods, and computer program products for categorizing a real-time monitoring service alert is provided. An example apparatus may include program code configured to cause the apparatus to retrieve the real-time monitoring service alert, the real-time monitoring service alert including a text string containing user generated content (UGC) text. In addition, the example apparatus may be configured to programmatically parse the text string of the real-time monitoring service alert to segregate the real-time monitoring service alert into an alert message problem component and an alert auxiliary details component. Further, the apparatus may be configured to determine, based on the alert message problem component, the alert auxiliary details component, and using an alert message machine learning model trained based on UGC transformed alert data, an alert message category of the real-time monitoring service alert.

Abstract: Apparatuses, methods, systems, or computer-readable storage medium for generating alert group summaries in software management platforms. An alert group comprising a plurality of alert data objects may be identified. One or more alert features associated with the alert group may be extracted based on the plurality of alert data objects and using one or more feature extraction models. Action-related communication content for the alert group may be retrieved. An alert group summary for the alert group may be generated using one or more machine learning models and based on an input data set comprising the one or more alert features and the action-related communication content.

Abstract: Methods, apparatuses, or computer-readable storage medium provide for intelligently generating post-incident reports. A post-incident indication associated with an incident may be received. Relevant post-incident data associated with the incident may be determined using one or more machine learning models and based on one or more enterprise applications. A post-incident report for the incident may be generated based on the relevant post-incident data. The post-incident report may be provided for display on a client computing device.

Abstract: Apparatuses, methods, systems, or computer-readable storage medium for generating alert group summaries in software management platforms. An alert group comprising a plurality of alert data objects may be identified. One or more alert features associated with the alert group may be extracted based on the plurality of alert data objects and using one or more feature extraction models. Action-related communication content for the alert group may be retrieved. An alert group summary for the alert group may be generated using one or more machine learning models and based on an input data set comprising the one or more alert features and the action-related communication content.

Abstract: Automated system for predicting software application incident-causing deployments using a ranking framework is provided. A plurality of candidate code deployment data objects for an incident comprising an affected service data object may be identified. For each candidate code deployment data object of the plurality of candidate code deployment data objects a semantic similarity score, a topological distance score, and a temporal score may be generated. The plurality of candidate code deployment data objects may be ranked using a ranking model and based on the semantic similarity score, the topological distance score, and the temporal score for each candidate code deployment data object.

Abstract: Automated system for predicting software application framework deployment changes for incident mitigation is provided. An incident indication for an incident may be received. One or more incident features associated with the incident may be extracted. One or more data objects associated with the incident may be identified based on the one or more incident features. Candidate incident mitigation dataset comprising a plurality of incident mitigating predictions may be generated based on relation data and a topology graph structure associated with the one or more data objects. A ranked candidate incident mitigation dataset may be generated using one or more ranking models. One or more incident mitigating predictions from the ranked candidate incident mitigation dataset may be selected based on a rank value associated with each of the one or more incident mitigating predictions.

Abstract: Methods, apparatuses, or computer program products that provide predictive inferences related to a graph representation of data via deep learning are disclosed herein. In some examples, graph data of a graphical data structure is transformed into a graph feature set, a deep learning model is applied to the graph feature set to generate a graph embedding structure for a portion of the graph data associated with a user identifier, and the graph embedding structure is provided to one or more machine learning models configured to generate one or more predictive inferences related to the graph data.

Abstract: Various embodiments disclosed herein are directed to a system, method, apparatus, and/or a computer program product that are configured to embody an alert management system that is adapted to efficiently analyze and cluster different types of alerts (e.g., heterogenous alerts) including system generated alerts and user generated alerts. In one example, system generated alerts are analyzed using one or more tag based clustering algorithms while user generated alerts are analyzed using one or more machine learning clustering models. In another example, the alert management system may be configured to improve alert clustering efficiency by using tag based clustering algorithms on alerts that satisfy a tag presence threshold while machine-learning based clustering models are used on alerts that do not satisfy the tag presence threshold. In various embodiments, confidence metrics are calculated and used to reduce the number of false positive alert clusters and to improve alert cluster quality.

Abstract: Various embodiments disclosed herein are directed to a system, method, apparatus, and/or a computer program product that are configured to programmatically analyze alert clusters and make recommendations to alert managers as to alert policy changes that might reduce alert volume without hindering the intended performance of the alert management system. For example, various embodiments are configured to programmatically determine if a threshold number of alerts within an alert cluster are insignificant and output an alert policy change recommendation interface that is configured to allow alert mangers to execute adjustments to underlying alert policies. In some embodiments, alert policy changes are recommended only when a threshold number of alerts within an alert cluster are insignificant and such threshold number of alerts are determined to be increasing within the monitored software application framework.

Abstract: Methods, apparatuses, or computer-readable storage medium provide for intelligently generating post-incident reports. A post-incident indication associated with an incident may be received. Relevant post-incident data associated with the incident may be determined using one or more machine learning models and based on one or more enterprise applications. A post-incident report for the incident may be generated based on the relevant post-incident data. The post-incident report may be provided for display on a client computing device.