Abstract: Systems, computer-implemented methods, and computer program products that can facilitate refinement of a predicted event based on explainability data are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an interpreter component that identifies a probable cause of a predicted event based on explainability data. The computer executable components can further comprise an enrichment component that executes a diagnostic analysis based on the probable cause.

Abstract: Systems, computer-implemented methods, and computer program products that can facilitate refinement of a predicted event based on explainability data are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an interpreter component that identifies a probable cause of a predicted event based on explainability data. The computer executable components can further comprise an enrichment component that executes a diagnostic analysis based on the probable cause.

Abstract: Systems, computer-implemented methods, and computer program products that can facilitate refinement of a predicted event based on explainability data are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an interpreter component that identifies a probable cause of a predicted event based on explainability data. The computer executable components can further comprise an enrichment component that executes a diagnostic analysis based on the probable cause.

Abstract: Embodiments relate to monitoring computing hardware in a computing infrastructure facility. Image data and environmental data are received and a current operational status for a computing hardware component is determined from the image data. A hardware operational status tracking model and environment tracking model for the computing hardware component are updated. Embodiments can perform a root cause analysis if the current operational status is a fault status to determine if the fault status was caused by environmental conditions.

Abstract: A trained classification model is executed, causing a classification of a first set of file system usage data into a set of categories comprising a trend category and a periodicity category. Responsive to the first set of file system usage data being classified into the trend category, a time series of the first set of file system usage data is generated. Responsive to the first set of file system usage data being classified into the periodicity category, using an anomaly detection model, an anomaly within the first set of file system usage data is detected. Responsive to predicting that the time series will exceed a threshold, a first reconfiguring of a file system resource is caused, altering a capacity of the file system. Responsive to detecting the anomaly, a second reconfiguring of the file system resource is caused, altering a capacity of the file system.

Abstract: Automated managing of a data center installation is provided. The managing includes evaluating, at least in part by image processing analysis, a captured image of at least a portion of the data center installation to identify a component-related deficiency within the data center installation. One or more measurements within a data center are used to determine an energy penalty due to the identified component-related deficiency within the data center installation, and an action to correct the component-related deficiency within the data center installation is initiated based on the energy penalty exceeding a predefined threshold.

Abstract: A system is disclosed for chatbot conversation construction, and includes user interface, coupled to a conversation construction and management platform, configured to provide entry of intents, associated responses and search terms, and to construct and store conversation controllers, each based at least in part on one or more of the intents, and build conversation flows, each based at least in part on one or more of the conversation controllers. An artificial intelligence (AI) connector platform couples to the conversation construction and management platform, and is configured to receive natural language (NL) search data from a chatbot channel, format the NL search data into an input protocol of a selectable NL processing engine and receive a response from the NLP engine and, based at least in part on one or more of the conversation flows, convert the NLP engine response to a personalized response, and send the personalized response to the chatbot channel.

Abstract: Systems, computer-implemented methods, and computer program products that can facilitate refinement of a predicted event based on explainability data are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an interpreter component that identifies a probable cause of a predicted event based on explainability data. The computer executable components can further comprise an enrichment component that executes a diagnostic analysis based on the probable cause.

Abstract: Embodiments relate to monitoring computing hardware in a computing infrastructure facility. Image data and environmental data are received and a current operational status for a computing hardware component is determined from the image data. A hardware operational status tracking model and environment tracking model for the computing hardware component are updated. Embodiments can perform a root cause analysis if the current operational status is a fault status to determine if the fault status was caused by environmental conditions.

Abstract: Automated managing of a data center installation is provided. The managing includes evaluating, at least in part by image processing analysis, a captured image of at least a portion of the data center installation to identify a component-related deficiency within the data center installation. One or more measurements within a data center are used to determine an energy penalty due to the identified component-related deficiency within the data center installation, and an action to correct the component-related deficiency within the data center installation is initiated based on the energy penalty exceeding a predefined threshold.

Abstract: A system is disclosed for chatbot conversation construction, and includes user interface, coupled to a conversation construction and management platform, configured to provide entry of intents, associated responses and search terms, and to construct and store conversation controllers, each based at least in part on one or more of the intents, and build conversation flows, each based at least in part on one or more of the conversation controllers. An artificial intelligence (AI) connector platform couples to the conversation construction and management platform, and is configured to receive natural language (NL) search data from a chatbot channel, format the NL search data into an input protocol of a selectable NL processing engine and receive a response from the NLP engine and, based at least in part on one or more of the conversation flows, convert the NLP engine response to a personalized response, and send the personalized response to the chatbot channel.

Abstract: A system, method and computer program product for optimizing total cost of ownership (TCO) of a piece of IT equipment, e.g., a hard drive or server, using predictive analytics. The data center environment monitors and measures a number of environment variables, including temperature, Relative Humidity, and corrosion. For each piece of hardware, several pieces of data are assigned, including a criticality measure, an operational cost (function of environment), a static replacement cost, and a downtime cost (function of time). For each piece of hardware, if it has not yet failed, the system predicts a time-to-failure using the environment variables. If predicted time-to-failure exceeds an expected reference life criteria, real time TCO analytics is performed to minimize data center energy usage and/or maximize operational cost-efficiency.

Abstract: A system and a method for enabling a fingerprint registration on a mobile device are provided. The mobile device includes a fingerprint registration unit configured to receive at least one first image of a fingerprint input in a first orientation, receive at least one second image of the fingerprint input in a second orientation, and generate a fingerprint image by stitching the at least one first image with the at least one second image such that the first orientation and the second orientation are substantially perpendicular to each other.

Abstract: Aspects include a method, system, and computer program product for determining a time to a threshold temperature of a device in a data center. A method includes measuring parameters for a device and the data center. A rate of change of temperature is determined based on the parameters. The change is compared to a change threshold. It is determined that a cooling system is operating below a threshold when the change is above the threshold. A first time is determined based on the rate of change of temperature and a machine learning model. The first and second time are compared, where the second time is a time to restore the cooling system above the threshold. A signal is transmitted when the first time is less than the second time. A cooling capacity is determined to have the temperature change of the device be equal to or less than the threshold.

Abstract: A system, method and computer program product for optimizing total cost of ownership (TCO) of a piece of IT equipment, e.g., a hard drive or server, using predictive analytics. The data center environment monitors and measures a number of environment variables, including temperature, Relative Humidity, and corrosion. For each piece of hardware, several pieces of data are assigned, including a criticality measure, an operational cost (function of environment), a static replacement cost, and a downtime cost (function of time). For each piece of hardware, if it has not yet failed, the system predicts a time-to-failure using the environment variables. If predicted time-to-failure exceeds an expected reference life criteria, real time TCO analytics is performed to minimize data center energy usage and/or maximize operational cost-efficiency.

Abstract: Aspects include a method, system, and computer program product for determining a time to a threshold temperature of a device in a data center. A method includes measuring parameters for a device and the data center. A rate of change of temperature is determined based on the parameters. The change is compared to a change threshold. It is determined that a cooling system is operating below a threshold when the change is above the threshold. A first time is determined based on the rate of change of temperature and a machine learning model. The first and second time are compared, where the second time is a time to restore the cooling system above the threshold. A signal is transmitted when the first time is less than the second time. A cooling capacity is determined to have the temperature change of the device be equal to or less than the threshold.

Abstract: Aspects include a method, system, and computer program product for determining a time to a threshold temperature of an electronic device in a data center. A method includes measuring parameters for an electronic device and the data center. A rate of change of temperature is determined for the device based on the parameters. The rate of change is compared to a rate of change threshold. It is determined that a cooling system is operating below a threshold when the rate of change is above the threshold. A first time is determined, where the first time is based the rate of change of temperature and a machine learning model. The first time and second time are compared, where the second time is a time to restore the cooling system to above the threshold. A signal is transmitted when the first time is less than the second time.

Abstract: A system and a method for enabling a fingerprint registration on a mobile device are provided. The mobile device includes a fingerprint registration unit configured to receive at least one first image of a fingerprint input in a first orientation, receive at least one second image of the fingerprint input in a second orientation, and generate a fingerprint image by stitching the at least one first image with the at least one second image such that the first orientation and the second orientation are substantially perpendicular to each other.

Abstract: A non-imaging diagnostic ultrasound system for carotid artery diagnosis has a two dimensional array probe with a low element count and relatively large element size which can cover an area of the carotid artery at its bifurcation. The elements are operated independently with no phasing, and detect Doppler flow spatially beneath each element. The system produces maps of carotid blood flow in two or three dimensions and can assemble an extended view of the flow by matching segments of the carotid flow as the probe is moved over the vessel. Once the carotid artery has been localized, the degree of stenosis is assessed by automated measurements of peak systolic velocity and blood flow turbulence.