Abstract: A method and system for improving an operation of an automated IT system is provided. The method includes identifying a minimum set of software applications associated with requirements of processes executed by a hardware device with respect to an IT system. A list of sub-features associated with software based solutions is generated and associated evaluation code is generated. Software features are identified and software operational solutions are generated. Gating criteria adjustment code for selecting additional software applications is generated and executed resulting in improved operation of the hardware device.

Abstract: A method and system for improving an operation of an automated IT system is provided. The method includes identifying pre-tested software applications associated with requirements of processes executed by a hardware device with respect to an IT system. A list of available software applications associated with required features is generated and each feature is defined such that the currently available software applications are configured to provide and execute the required features. Evaluation code is executed and a resulting a list of validated software applications is generated. A list of short listed software applications and identification software code enabling an automated encoder learning process are generated. A software operational solution is identified and modification code is generated and executed code resulting in improved operation of the validated software applications and the hardware device.

Abstract: A method and system for improving an operation of an automated IT system is provided. The method includes identifying a minimum set of software applications associated with requirements of processes executed by a hardware device with respect to an IT system. A list of sub-features associated with software based solutions is generated and associated evaluation code is generated. Software features are identified and software operational solutions are generated. Gating criteria adjustment code for selecting additional software applications is generated and executed resulting in improved operation of the hardware device.

Abstract: The present invention is a method and framework for protocol to optimize edge side dynamic response system with context propagation for IoT. The protocol is a composition of a set of predefined routines, called ‘parlance’, that control the lifecycle of the meshed devices. The master has characteristics to store database as block chains, encryption using hash function, run defined duty cycles. The edge devices have characteristics to receive parlance, execute parlance, provide data to mantles.

Abstract: A system, method and program product for evaluating energy suppliers from a set of energy suppliers to meet demand for a distribution grid. A system is disclosed that includes: a bid volatility index (BVI) calculator that determines a BVI for each supplier for an offer; a profiling system that profiles each supplier with a set of predictor variables; a ranking system that determines a winning bid probability of each supplier based on the predictor variables; a modeling system that refines the winning bid probability based on changes to at least one predictor variable; and a scoring system that determines a confidence score for each supplier, wherein the confidence score indicates a confidence level that the supplier can meet the offer.

Abstract: A portion of data is received from each sensor of a plurality of sensors. An emergency level corresponding to each metric of a plurality of metrics is calculated, where the calculated emergency level is determined based on the portion of data received from the sensors. A total emergency level corresponding to the current situation is calculated, where the calculation of the total emergency level is based on the emergency level calculated for each metric. It is determined whether the total emergency level is above a critical threshold, where the critical threshold is selected by the user. If it is determined that the total emergency level is above the critical threshold, an alert is sent to a party such as one or more local authorities.

Abstract: A method uses random walk traversal(s) (for example, Lévy's flight algorithm) of a story data graph to help select and/or order nodes for automated story writing purposes. Dialogue metadata (for example, inverted quotation marks) is added, to indicate natural language dialogue in nodes of the story data graph. A cyclic, non-directed story data graph is partitioned into non-cyclic tree(s) (also called tree logical data structures) using nodes and connections from the story data graph.

Abstract: Aspects optimize competitive bidding processes for energy suppliers as a function of energy block denominations. Subset energy block sizes are defined with different quantities of energy that total up to a specified quantity of energy, as a function of matching block sizes to bidding size preferences indicated by prior supplier bidding activities of different energy suppliers. Likely dispersion distributions of bids of offered energy by the energy suppliers are determined across each of the different energy block sizes as a function of likelihoods to bid for each of the energy block sizes at the specified price. A subset group of the energy blocks are identified that have likely dispersion distribution values less than a threshold dispersion value. Energy bids are allocated to the suppliers according to their likelihood to bid in the energy quantities of the subset of the energy blocks.

Abstract: Machine logic based automated story writing technology that: respectively represents content data sources for potential use in an automated story as nodes in a graph; includes emotion metadata in each given node of the graph, with the emotion metadata relating to “emotions” (“sentiments”) that are likely to be caused in story consumers and/or story characters if the content corresponding to the given node is used in an automated story; use the emotion metadata to help select a sub-set of nodes whose respectively associated content is to be used in an automated story; and use the emotion metadata to help select the order of the use of the content nodes in an automated story. The selection of the nodes based on emotion metadata and/or the ordering of the nodes based on emotion metadata may be further based on seed information (for example, story seed information supplied by a human co-author).

Abstract: A huge volume of unstructured content is available on the internet. Social media websites, news outlets, subject matter expert sites, forums, government organization sites, non-government organization sites, etc., collectively provide a rich source of raw material for any kind of story writing, for example, for movies, novels, television, etc. In some embodiments of the present invention, content is intelligently searched from diverse sources. Embodiments of the present invention make use of such unstructured content, to provide raw material upon which to base a cohesive and appealing story, in part by applying graphing theory to: (i) represent content gathered in the search as a graph, with each element of content assigned to a node of the graph; (ii) qualitatively link the nodes; and/or (iii) identify important nodes which potentially become central to a storyline.

Abstract: A method and associated systems for a self-learning energy switch. The switch creates an array of cognitive models for each candidate energy source. Each array returns a probability that its corresponding source is the most cost-effective and operationally suitable energy supplier at that time. Each model in an array contributes to the array's returned probability as a function of a corresponding class of decision-making factors. The system fine-tunes the models by weighting them as functions of extrinsic evidentiary information that may imply future behavior of the decision-making factors and combines each model's returned probabilities to select an optimal energy source. The system then automatically routes power from the optimal source to a consumer's energy-consuming premises. This self-learning procedure repeats indefinitely, continuously tuning the models in response to identifying additional extrinsic evidence and reasons why the system's previous energy selections were either optimal or non-optimal.

Abstract: A computer-implemented method includes: receiving, by a computing device, analytics data at a feeder level of a utility transmission and distribution system; generating, by the computing device, a confidence score for a feeder based on the analytics data at the feeder level; receiving, by the computing device, analytics data at a transformer level of the utility transmission and distribution system; generating, by the computing device, a confidence score for a transformer associated with the feeder based on the analytics data at the transformer level and the confidence score of the feeder; receiving, by the computing device, analytics data for customers associated with the transformer; generating, by the computing device, confidence scores for the customers based on the analytics data and the confidence score of the transformer; and outputting, by the computing device, information regarding the confidence scores for the customers for determining the effectiveness of a demand response (DR) program.

Abstract: Energy load forecasts are generated via model(s) for the grid hierarchy elements for different forecast time scale periods as a function of different sets of prioritized contextual influencing factors for respective associated combinations of grid hierarchy elements and forecast time scale periods. Relative priority values of the sets of the contextual influencing factors are iteratively weighted until a revised energy load forecast generated as a function of the weighted values via the model(s) is within a threshold value of a historic energy load data value for the associated combination of the grid hierarchy element and forecast time scale period.

Abstract: In an approach to managing data quality and compliance, a computer retrieves data within a business enterprise system. The computer applies a set of rules to the data, wherein the application of the set of rules to the data produces a plurality of mapped data, and the computer aggregates the plurality of mapped data into a plurality of results. The computer then reports the plurality of results for a data display.

Abstract: A portion of data is received from each sensor of a plurality of sensors. An emergency level corresponding to each metric of a plurality of metrics is calculated, where the calculated emergency level is determined based on the portion of data received from the sensors. A total emergency level corresponding to the current situation is calculated, where the calculation of the total emergency level is based on the emergency level calculated for each metric. It is determined whether the total emergency level is above a critical threshold, where the critical threshold is selected by the user. If it is determined that the total emergency level is above the critical threshold, an alert is sent to a party such as one or more local authorities.

Abstract: Modeling a governance process of optimizing a portfolio of services in a governed SOA and generating, in dependence upon the modeled governance process, one or more automation modules, each automation module including a module of computer program instructions that supports performance of one or more steps of the modeled governance process, where the modeled governance process includes determining whether a collection of service artifacts matches one or more service artifacts stored in a service registry; if no service artifacts stored in the service registry match the collection of service artifacts, initiating creation of a service in accordance with the collection of service artifacts; if one or more service artifacts stored in the service registry match the collection of service artifacts: selecting, in dependence upon predefined action selection criteria, a service administration action to perform; obtaining approval of the selected service administration action; and initiating the selected service admini

Abstract: Modeling a governance process of optimizing a portfolio of services in a governed SOA and generating, in dependence upon the modeled governance process, one or more automation modules, each automation module including a module of computer program instructions that supports performance of one or more steps of the modeled governance process, where the modeled governance process includes determining whether a collection of service artifacts matches one or more service artifacts stored in a service registry; if no service artifacts stored in the service registry match the collection of service artifacts, initiating creation of a service in accordance with the collection of service artifacts; if one or more service artifacts stored in the service registry match the collection of service artifacts: selecting, in dependence upon predefined action selection criteria, a service administration action to perform; obtaining approval of the selected service administration action; and initiating the selected service admini

Abstract: Modeling a governance process of establishing a subscription to a deployed service in a governed Service Oriented Architecture (‘SOA’) and generating, in dependence upon the modeled governance process, one or more automation modules, each automation module comprising a module of computer program instructions that, when executed by a computer processor, supports performance of one or more steps of the modeled governance process, where the modeled governance process includes collecting available service artifacts associated with a deployed service; determining whether the deployed service meets predefined functional requirements; if the deployed service meets the predefined functional requirements, determining whether the deployed service meets predefined non-functional requirements if the deployed service meets the predefined non-functional requirements, creating a subscription request; requesting approval of the subscription request; and if approval is received, creating a subscription to the deployed service

Abstract: Modeling a governance process of creating a new version of a service in a governed SOA and generating, in dependence upon the modeled governance process, automation modules that include modules of computer program instructions that, when executed by a computer processor, support performance of one or more steps of the modeled governance process and where the modeled governance process includes reviewing requested updates to a deployed service; determining, in dependence upon the requested updates and a specification of a present version of the deployed service, whether a new version of the deployed service is required; if a new version of the deployed service is required, determining whether the present version of the deployed service will be subsumed by the new version; and if the present version of the deployed service will be subsumed by the new version, retiring the present version of the deployed service.

Abstract: Modeling a governance process of creating a new version of a service in a governed SOA and generating, in dependence upon the modeled governance process, automation modules that include modules of computer program instructions that, when executed by a computer processor, support performance of one or more steps of the modeled governance process and where the modeled governance process includes reviewing requested updates to a deployed service; determining, in dependence upon the requested updates and a specification of a present version of the deployed service, whether a new version of the deployed service is required; if a new version of the deployed service is required, determining whether the present version of the deployed service will be subsumed by the new version; and if the present version of the deployed service will be subsumed by the new version, retiring the present version of the deployed service.