Abstract: Embodiments relate to monitoring an information technology (IT) environment having a plurality of domains through key performance indicator (KPI) data. In response to detection of a technical health problem, a first KPI related to the problem is identified. A root cause analysis is performed on the identified KPI generating a knowledge graph. A second KPI related to the first KPI is identified through the discovery of a correlation between the two identified KPIs. A diagnosis is generated for the technical health problem within the IT environment based on the discovered hidden correlation between the first KPI and second KPI. The generated diagnosis includes the root cause of the technical health issue.

Abstract: A computer implemented method is provided that includes using historic migration data to label key performance indicators (KPIs) in a migration model including a scale that indicates a level of successful migration to a remote provider. Employing the migration model to predict successful migration of a local application having one or more of said one or more of local key performance indicators for the local application. Migrating the local application to a remote provider when the model to predict successful migration indicates a greater than threshold value for successful migration.

Abstract: A method of mitigating risks in a service level agreement (SLA), including: identifying relationships between an infrastructure and an application associated with the SLA; identifying changes to a technological environment of the SLA based on collected data associated with the SLA and the identified relationships, the collected data including the infrastructure and the application; establishing a baseline for a service level objective (SLO) of the SLA by analyzing the collected data of the infrastructure and the application; determining risk impact to the SLA based on an assessment of requirements to the SLO and the changes to the technological environment of the SLA; generating a solution option for the SLA by applying an explainable artificial intelligence (XAI) model based on processing the risk impact with the baseline in the XAI model; and updating the SLA based on the solution option.

Abstract: A user experience theme description is obtained, along with a new user experience feature image set. The theme description and new user experience feature image set are input into a generative adversarial network (GAN). The GAN is trained to output multiple user experience designs based on the new feature image set. The multiple designs are displayed on an electronic display device that includes an eye gaze tracking system. User interface elements and their corresponding positions within a user interface are identified based on eye gaze of a user towards the electronic display device. The position and type of user interface elements are compared between a desired user interface design and a generated user interface design. Errors between the desired user interface design and the generated user interface design are input as feedback into the GAN to further enhance the results.

Abstract: A method, computer program product, and system include a processor(s) obtaining an instruction to perform an inspection of a given type at a geographic site. The processor(s) deploys a robotic drone to the geographic site, wherein based on the deployment, the robotic drone performs a contextual analysis on the geographic site to identify a use case and to collect locational data. The processor(s) obtains the locational data. Based on the locational data, the given type of the inspection, and the use case, the processor(s) generates an inspection plan comprising tasks. The processor(s) identifies robotic drone(s) to complete the tasks and distributes the tasks. The robotic drone( )automatically self-optimize/s to complete the tasks. The processor(s) obtain the collected data from the self-optimized identified one or more robotic drones. The processor(s) analyze the collected data to identify issue(s) at geographic site.

Abstract: Disclosed are techniques for dynamically prioritizing subsets of data within datasets based on context. Historical analysis logs, the underlying datasets for the historical analysis logs, and context data are used to train a machine learning model to determine subsets of data within an input dataset when provided the input dataset and a context information input set. When an input dataset and context information input set are received, the machine learning model determines subsets of data (if any) that should be prioritized for processing ahead of other sets of data in the input dataset, based on the context information input set. Subsets of data within an input dataset with higher priority values are processed before other sets of data within the input dataset.

Abstract: An approach for providing solutions to ad in the process recovery in manufacturing systems to resume manufacturing activity is disclosed. The outcome of the approach can include the following advantages, minimizing waste, reducing cost of recovery, and increasing efficiency in a manufacturing process. The approach includes collecting initial factory data; identifying and categorizing one or more shop floor activities; determining initial recovery process; creating a digital twin copy of the factory; performing one or more initial simulation scenarios; generating factory improvement plan; and executing factory improvement plan.

Abstract: A processor may receive object data associated with a position and an orientation of a first object in an environment from IoT sensors. The processor may generate a digital twin simulation of the first object. In some embodiments, the digital twin simulation may include data associated with the relative positions and orientations of one or more other objects to the first object. The processor may calculate forces acting on the first object. The processor may identify whether the first object is in a state of instability.

Abstract: A system may include a memory and a processor in communication with the memory. The processor may be configured to perform operations. The operations may include collecting organism data and analyzing the organism data. The operations may also include predicting a organism movement pattern based on the organism data and relaying the organism movement pattern to a participant.

Abstract: A method for optimizing device-to-device communication protocol selection in an edge computing environment is provided. The method includes: receiving a request for a service from a user device, wherein the computing system is one of plural edge computing devices in an edge computing environment; determining computational tasks performed in providing the service; selecting, using a machine learning model, a set of the edge computing devices to perform the computational tasks and communication protocols for the set of the edge computing devices to use while performing the computational tasks, wherein the machine learning model is configured to select the set of the edge computing devices and the communication protocols based on minimizing a time to perform the computational tasks; and sending instructions to perform the computational tasks, thereby causing the set of the edge computing devices to perform the service in response to the request from the user device.

Abstract: A tool for providing contextual policy-based computer vision control. The tool identifies one or more camera devices in an environment. The tool determines a plurality of vision areas in a field of view within the environment. The tool determines one or more contextual policies for the plurality of vision areas in the field of view. The tool determines one or more vision augmentations for the plurality of vision areas based, at least in part, on an aggregate computer vision capability of the one or more camera devices in the environment and the one or more contextual policies for the plurality of vision areas in the field of view. The tool applies the one or more vision augmentations to at least one of the one or more camera devices in the environment.

Abstract: Provided is a method for generating a data backup strategy for a computer system. The method comprises receiving an event related to a change in a computer system. The method further comprises applying regression techniques on historical data related to previous events for the computer system to determine a failure prediction score for the computer system. The method further comprises calculating a set of backup parameters for performing a backup of data of the computer system. The method further comprises generating a score for the backup using the set of backup parameters. The method further comprises determining a backup strategy for the computer system based on the score.

Abstract: An approach for securely accessing self-sovereign data via a bot-chain ledger may be provided. A bot may request access to a piece distributed data at a bot-chain client. A bot registry service may validate the requesting bot is registered with the bot-ledgering client. The bot-ledgering client may generate a token for the requesting bot and provide the identity of a data bot with permission to access the piece of distributed data. A data bot may request to read the piece of distributed data at the bot-ledgering client. The bot-ledgering client may verify the data bot is registered with the bot-chain. The bot-ledgering client may generate an access token and send it to the data bot.

Abstract: An approach for securely accessing self-sovereign data via a bot-chain ledger may be provided. A bot may request access to a piece distributed data at a bot-chain client. A bot registry service may validate the requesting bot is registered with the bot-ledgering client. The bot-ledgering client may generate a token for the requesting bot and provide the identity of a data bot with permission to access the piece of distributed data. A data bot may request to read the piece of distributed data at the bot-ledgering client. The bot-ledgering client may verify the data bot is registered with the bot-chain. The bot-ledgering client may generate an access token and send it to the data bot.

Abstract: A computer implemented method is provided that includes using historic migration data to label key performance indicators (KPIs) in a migration model including a scale that indicates a level of successful migration to a remote provider. Employing the migration model to predict successful migration of a local application having one or more of said one or more of local key performance indicators for the local application. Migrating the local application to a remote provider when the model to predict successful migration indicates a greater than threshold value for successful migration.

Abstract: A service catalog system where patterns in a service catalog (respectively corresponding to various available services) are modified based on consumption data (that is, how other users have used the services in the past). Also, a service catalog system where a pattern is added to a service catalog based on consumption data. Some embodiments use artificial intelligence (AI), machine learning (ML), cognitive processing and/or advanced analytics to modify existing patterns and/or recommend new patterns to be added to the service catalog.

Abstract: Computer systems that use artificial intelligence to predict strategies, scenarios and or game plans for sports games that are already in progress. In some embodiments a feedback loop is used so that the artificial intelligence takes into account events occurring in and/or around the game as they occur to provide updated predictions.

Abstract: In an approach for dynamically determining a suitable SME for a particular critical situation, a processor receives an initial problem statement. A processor constructs a collaboration channel associated with the initial problem statement. A processor identifies an SME based on a rating that associates the SME with the initial problem statement. A processor sends an invitation to the SME to join the collaboration channel.

Abstract: A method and system for automatically generating a virtual assisted task is provided. The method includes establishing secure connections between a hardware device and databases external to the hardware device. A group of tasks associated with an individual are identified and available timeframes associated with an availability of the individual are determined. A first potential score and maximum score for each task are computed. The first potential score is compared to each maximum potential score and in response, a subset of tasks are automatically selected. The subset of tasks are mapped to the available timeframes and results of the mapping a cached within a caching circuit of the hardware device. Results of the caching are presented via the hardware device.

Abstract: Embodiments relate to monitoring an information technology (IT) environment having a plurality of domains through key performance indicator (KPI) data. In response to detection of a technical health problem, a first KPI related to the problem is identified. A root cause analysis is performed on the identified KPI generating a knowledge graph. A second KPI related to the first KPI is identified through the discovery of a correlation between the two identified KPIs. A diagnosis is generated for the technical health problem within the IT environment based on the discovered hidden correlation between the first KPI and second KPI. The generated diagnosis includes the root cause of the technical health issue.