Abstract: Embodiments describe an approach for leveraging Bots across various layers of an enterprise information technology system for reducing mean time to find problems (MTFP). The approach comprising: determining if one or more system Bots can identify one or more issues in an enterprise information technology system. Escalating the one or more issues to one or more process Bots. Invoking one or more MTFP computation engines from related Bots in communication with the one or more process Bots. Identifying the one or more issues in the enterprise information technology system by the one or more MTFP computation engines. Updating a knowledge repository with attributes of the identified one or more issues, wherein the one or more process Bots can cognitively learn from the data stored on the knowledge repository; and outputting the one or more identified issues to a user.

Abstract: Anomaly detection using zonal parameter characteristics and non-linear scoring is provided. Anomalous behavior characteristics of a parameter and group of parameters are identified within time series data using an optimal artificial intelligence model of a plurality of artificial intelligence models. Anomalies are detected based on the anomalous behavior characteristics of the parameter and the group of parameters within the time series data. The anomalies are classified into a corresponding anomaly category. A root cause of the anomalies is determined based on the corresponding anomaly category. One or more action steps are performed to remediate the root cause of the anomalies.

Abstract: A method, system and computer program product for calculating power usage effectiveness (PUE) in a data center generate, based on a historical data of the data center, a template for each data center parameter of a plurality of data center parameters and uses the generated template to calculate an expected value of each data center parameter that can be subsequently used to compute and optimize the power usage effectiveness in the data center.

Abstract: A method of predicting future performance of a data center battery system includes receiving information related to a data center battery system in real time, analyzing the received information in real time, predicting a future performance of the data center battery system, providing the predicted future performance of the data center battery system to a data center control system, and optimizing the data center battery system based on the predicting.

Abstract: In an approach for generating hardware failure labels, a processor receives sensor data from a plurality of sensors associated with a hardware system. A processor calculates an adaptive stress factor, wherein the adaptive stress factor is a dynamic selection model. A processor calculates an adaptive stress time window, wherein the adaptive stress time window is a spatial distribution of the adaptive stress factor. A processor calculates a relative duty cycle, wherein the relative duty cycle is a first function of an internal state of the hardware system, a type of input to the hardware system, the adaptive stress factor, and the adaptive stress time window. A processor generates a failure label, wherein the failure label is calculated as a second function of the relative duty cycle and a design duty cycle.

Abstract: An embodiment includes duplicating an input dataset being input to a model predictive control (MPC) module for input to a first Hierarchical Temporal Memory (HTM) network. The embodiment also includes generating system behavior data using the MPC module for characteristic data of the input dataset. The embodiment also includes generating first HTM prediction data from the input dataset and the system behavior data using the first HTM network, the first HTM prediction data comprising predictions for respective dimensions of the system. The embodiment also includes generating second HTM prediction data from the first HTM prediction data and system output data using a second HTM network, the second HTM prediction data comprising a distinction between the first HTM prediction and the system output data. Finally, the embodiment includes determining that the distinction of the second HTM prediction data indicates an anomaly and adjusting system input data based on the anomaly.

Abstract: Disclosed embodiments provide techniques for technical support. Previously reported problem reports are analyzed and symptoms are extracted. Solutions are associated with the previously reported problem reports. A newly submitted user problem is analyzed and symptoms are extracted and compared with the symptoms of the previously reported problems. Solutions are then associated with the user problem based on relevance to symptoms, product type, and/or other factors.

Abstract: A method is provided to manage economics and operational dynamics of various information technology (IT) systems. A computer collects data indicative of operation of a plurality of hardware components and collects data indicative of operation of a plurality of software components. The computer creates a first qualitative value representing a hardware status of the plurality of the hardware components and a second qualitative value representing a software status of the plurality of the software components. The first and second qualitative values are displayed in graphical form for evaluation by a system operator, and the computer computes a probability of life expectancy for the plurality of hardware components and the plurality of software components based on said first and second qualitative values and utilizing cognitive and artificial intelligence based calculations to determine the probability.

Abstract: Anomaly detection using zonal parameter characteristics and non-linear scoring is provided. Anomalous behavior characteristics of a parameter and group of parameters are identified within time series data using an optimal artificial intelligence model of a plurality of artificial intelligence models. Anomalies are detected based on the anomalous behavior characteristics of the parameter and the group of parameters within the time series data. The anomalies are classified into a corresponding anomaly category. A root cause of the anomalies is determined based on the corresponding anomaly category. One or more action steps are performed to remediate the root cause of the anomalies.

Abstract: An embodiment includes duplicating an input dataset being input to a model predictive control (MPC) module for input to a first Hierarchical Temporal Memory (HTM) network. The embodiment also includes generating system behavior data using the MPC module for characteristic data of the input dataset. The embodiment also includes generating first HTM prediction data from the input dataset and the system behavior data using the first HTM network, the first HTM prediction data comprising predictions for respective dimensions of the system. The embodiment also includes generating second HTM prediction data from the first HTM prediction data and system output data using a second HTM network, the second HTM prediction data comprising a distinction between the first HTM prediction and the system output data. Finally, the embodiment includes determining that the distinction of the second HTM prediction data indicates an anomaly and adjusting system input data based on the anomaly.

Abstract: A method, system and computer program product for calculating power usage effectiveness (PUE) in a data center generate, based on a historical data of the data center, a template for each data center parameter of a plurality of data center parameters and uses the generated template to calculate an expected value of each data center parameter that can be subsequently used to compute and optimize the power usage effectiveness in the data center.

Abstract: A computer-implemented method, system, and non-transitory computer program product for maintaining a system. A domain of the system is identified. Problem information identifying a problem in the system is captured. Key performance indicators are obtained from a historical model database for the identified domain. An unsupervised model is applied to the key performance indicators to identify historical solutions to historical problems that are similar to the problem in the system. A linear complexity model is used to identify potential solutions for the problem as an historical solution for each historical problem that has the lowest combination of time-cost complexity, resource-cost complexity, and recurrence frequency for the historical problem. A real-time objective function is used to select a solution to the problem from the potential solutions. A maintenance operation to be performed on the system to implement the solution and fix the problem is identified.

Abstract: Disclosed embodiments provide techniques for technical support. Previously reported problem reports are analyzed and symptoms are extracted. Solutions are associated with the previously reported problem reports. A newly submitted user problem is analyzed and symptoms are extracted and compared with the symptoms of the previously reported problems. Solutions are then associated with the user problem based on relevance to symptoms, product type, and/or other factors.

Abstract: A method is provided to manage economics and operational dynamics of various information technology (IT) systems. A computer collects data indicative of operation of a plurality of hardware components and collects data indicative of operation of a plurality of software components. The computer creates a first qualitative value representing a hardware status of the plurality of the hardware components and a second qualitative value representing a software status of the plurality of the software components. The first and second qualitative values are displayed in graphical form for evaluation by a system operator, and the computer computes a probability of life expectancy for the plurality of hardware components and the plurality of software components based on said first and second qualitative values and utilizing cognitive and artificial intelligence based calculations to determine the probability.

Abstract: A method of predicting future performance of a data center battery system includes receiving information related to a data center battery system in real time, analyzing the received information in real time, predicting a future performance of the data center battery system, providing the predicted future performance of the data center battery system to a data center control system, and optimizing the data center battery system based on the predicting.

Abstract: A method is provided to manage economics and operational dynamics of various information technology (IT) systems. A computer collects data indicative of operation of a plurality of hardware components and collects data indicative of operation of a plurality of software components. The computer creates a first qualitative value representing a hardware status of the plurality of the hardware components and a second qualitative value representing a software status of the plurality of the software components. The first and second qualitative values are displayed in graphical form for evaluation by a system operator, and the computer computes a probability of life expectancy for the plurality of hardware components and the plurality of software components based on said first and second qualitative values and utilizing cognitive and artificial intelligence based calculations to determine the probability.

Abstract: Embodiments describe an approach for leveraging Bots across various layers of an enterprise information technology system for reducing mean time to find problems (MTFP). The approach comprising: determining if one or more system Bots can identify one or more issues in an enterprise information technology system. Escalating the one or more issues to one or more process Bots. Invoking one or more MTFP computation engines from related Bots in communication with the one or more process Bots. Identifying the one or more issues in the enterprise information technology system by the one or more MTFP computation engines. Updating a knowledge repository with attributes of the identified one or more issues, wherein the one or more process Bots can cognitively learn from the data stored on the knowledge repository; and outputting the one or more identified issues to a user.

Abstract: A computer-implemented method, system, and non-transitory computer program product for fault handling for an information technology system. Historical data for the information technology system is read from a database. A continued fraction model is applied to the historical data to generate spatial metrics. Real-time parameters for the information technology system are received. Jacobian modeling is applied to the real-time parameters to generate state space metrics. The spatial metrics and the state space metrics are combined using a recurrent space transformer to identify an actionable event in the information technology system. The recurrent space transformer maps the spatial metrics and the state space metrics to a common space. An alert is generated in response to identifying the actionable event in the information technology system.

Abstract: A computer-implemented method, system, and non-transitory computer program product for maintaining a system. A domain of the system is identified. Problem information identifying a problem in the system is captured. Key performance indicators are obtained from a historical model database for the identified domain. An unsupervised model is applied to the key performance indicators to identify historical solutions to historical problems that are similar to the problem in the system. A linear complexity model is used to identify potential solutions for the problem as an historical solution for each historical problem that has the lowest combination of time-cost complexity, resource-cost complexity, and recurrence frequency for the historical problem. A real-time objective function is used to select a solution to the problem from the potential solutions. A maintenance operation to be performed on the system to implement the solution and fix the problem is identified.

Abstract: A computer-implemented method, system, and non-transitory computer program product for fault handling for an information technology system. Historical data for the information technology system is read from a database. A continued fraction model is applied to the historical data to generate spatial metrics. Real-time parameters for the information technology system are received. Jacobian modeling is applied to the real-time parameters to generate state space metrics. The spatial metrics and the state space metrics are combined using a recurrent space transformer to identify an actionable event in the information technology system. The recurrent space transformer maps the spatial metrics and the state space metrics to a common space. An alert is generated in response to identifying the actionable event in the information technology system.