Abstract: The disclosure herein describes predicting potential impact of issues reported in incident ticket data on infrastructure element. A ticket manager component includes an impact model utilizing machine learning to analyze real-time event and metric data with incident-related data to generate predicted impact data. The predicted impact data identifies potentially impacted infrastructure elements, such as, potentially impacted users, predicted infrastructure components impacted by the issue and/or an updated time-period associated with the issue. The ticket manager component creates labeled incident tickets by updating user-generated incident tickets with additional data generated by the impact model, including predicted impact data and/or additional details associated with the issue. The labeled incident tickets are provided back to the model as training data to further refine predictions generated by the model.

Abstract: A method of determining physical real estate utilization can begin with receiving network and security data of an enterprise. A sign of work can be identified from the network and security data. For each sign of work, an Internet Protocol (IP) address and user identifier can be determined from the network and security data associated with the sign of work. Further, one or more user characteristics associated with the user identifier can be determined, and a physical location of the user at a time can be determined based on the IP address. The user characteristics and the physical location can be stored. Insights on physical real estate utilization can be generated based on at least two of: user characteristics of each user, the physical location of each user, and temporal data from the received network and security data, and a set of the insights can be output.

Abstract: The disclosure herein describes predicting potential impact of issues reported in incident ticket data on infrastructure element. A ticket manager component includes an impact model utilizing machine learning to analyze real-time event and metric data with incident-related data to generate predicted impact data. The predicted impact data identifies potentially impacted infrastructure elements, such as, potentially impacted users, predicted infrastructure components impacted by the issue and/or an updated time-period associated with the issue. The ticket manager component creates labeled incident tickets by updating user-generated incident tickets with additional data generated by the impact model, including predicted impact data and/or additional details associated with the issue. The labeled incident tickets are provided back to the model as training data to further refine predictions generated by the model.

Abstract: The disclosure herein describes predicting potential impact of issues reported in incident ticket data on infrastructure element. A ticket manager component includes an impact model utilizing machine learning to analyze real-time event and metric data with incident-related data to generate predicted impact data. The predicted impact data identifies potentially impacted infrastructure elements, such as, potentially impacted users, predicted infrastructure components impacted by the issue and/or an updated time-period associated with the issue. The ticket manager component creates labeled incident tickets by updating user-generated incident tickets with additional data generated by the impact model, including predicted impact data and/or additional details associated with the issue. The labeled incident tickets are provided back to the model as training data to further refine predictions generated by the model.

Abstract: A method of determining physical real estate utilization can begin with receiving network and security data of an enterprise. A sign of work can be identified from the network and security data. For each sign of work, an Internet Protocol (IP) address and user identifier can be determined from the network and security data associated with the sign of work. Further, one or more user characteristics associated with the user identifier can be determined, and a physical location of the user at a time can be determined based on the IP address. The user characteristics and the physical location can be stored. Insights on physical real estate utilization can be generated based on at least two of: user characteristics of each user, the physical location of each user, and temporal data from the received network and security data, and a set of the insights can be output.

Abstract: The disclosure herein describes determining computing assets associated with a service. Metadata from one or more computing assets of a computing system is collected. One or more alias terms are identified in the collected metadata of the one or more computing assets, wherein the alias terms are associated with the service. Service association scores are generated for the one or more computing assets based on the identified one or more alias terms and a term-score mapping. Each service association score indicates a likelihood that a computing asset is used for a service. A subset of computing assets of the one or more computing assets is identified. The subset of computing assets includes computing assets with service association scores that exceed a service association threshold. Computing assets of the service are identified based on the identified subset, thereby reducing the need for manual identification of asset-service associations.

Abstract: The disclosure herein describes predicting potential impact of issues reported in incident ticket data on infrastructure element. A ticket manager component includes an impact model utilizing machine learning to analyze real-time event and metric data with incident-related data to generate predicted impact data. The predicted impact data identifies potentially impacted infrastructure elements, such as, potentially impacted users, predicted infrastructure components impacted by the issue and/or an updated time-period associated with the issue. The ticket manager component creates labeled incident tickets by updating user-generated incident tickets with additional data generated by the impact model, including predicted impact data and/or additional details associated with the issue. The labeled incident tickets are provided back to the model as training data to further refine predictions generated by the model.

Abstract: A geofence computing device, in communication with a user device and a payment processor, includes a memory and a processor in communication with the memory. The geofence computing device processor is configured to receive, from the user device, geofence data including a first user defined geofence zone having a first geographic area within a first geographic boundary, store the geofence data within the memory, receive, from the payment processor, from authorization request message transaction data representing a payment transaction initiated with a merchant by a cardholder associated with the payment processor, determine a geographic location of the merchant and a residence of the cardholder, compare the merchant geographic location and the cardholder residence geographic location to the first user defined geofence zone, and alert the user of the payment transaction based on the comparison. The transaction data includes at least one of a merchant location and a merchant identifier.

Abstract: The disclosure herein describes determining computing assets associated with a service. Metadata from one or more computing assets of a computing system is collected. One or more alias terms are identified in the collected metadata of the one or more computing assets, wherein the alias terms are associated with the service. Service association scores are generated for the one or more computing assets based on the identified one or more alias terms and a term-score mapping. Each service association score indicates a likelihood that a computing asset is used for a service. A subset of computing assets of the one or more computing assets is identified. The subset of computing assets includes computing assets with service association scores that exceed a service association threshold. Computing assets of the service are identified based on the identified subset, thereby reducing the need for manual identification of asset-service associations.