Abstract: A power-distribution routing system of a power-transmission company receives a request to route electrical power to users through a power-grid infrastructure during a specified future period of time. The system retrieves time-stamped blockchain data that identifies past fluctuations in energy demand, service agreements between energy companies, and energy-production and demand-fulfilment histories of energy-generating sources like power plants. The system also retrieves extrinsic contextual and socioeconomic data from online sources and various business applications. An artificially intelligent cognitive framework uses a sliding-frame mechanism to infer patterns in the rate of change of user demand during past time periods similar to the period specified by the request. The system ranks each source by its demonstrated ability to satisfy the patterns of demand in consideration of the contextual data.

Abstract: Techniques for text evaluation are provided. A curated dataset comprising a plurality of textual documents is received. A tree of classifiers is trained, based on the curated dataset, to identify use cases. A feature graph model is generated, based on the curated dataset, to determine textual similarity. A new document is received, and a plurality of requirements is extracted from the new document. For each requirement, one or more vector scores are generated by evaluating the requirement using the tree of classifiers, one or more feature scores are generated by evaluating the requirement using the feature graph model, and one or more use cases are identified for the new textual document based on the one or more vector scores and the one or more feature scores. An implementation is generated for the new document based on the one or more use cases.

Abstract: A power-distribution routing system of a power-transmission company receives a request to route electrical power to users through a power-grid infrastructure during a specified future period of time. The system retrieves time-stamped blockchain data that identifies past fluctuations in energy demand, service agreements between energy companies, and energy-production and demand-fulfilment histories of energy-generating sources like power plants. The system also retrieves extrinsic contextual and socioeconomic data from online sources and various business applications. An artificially intelligent cognitive framework uses a sliding-frame mechanism to infer patterns in the rate of change of user demand during past time periods similar to the period specified by the request. The system ranks each source by its demonstrated ability to satisfy the patterns of demand in consideration of the contextual data.

Abstract: A method includes: determining, by a computing device, transactions of a monolithic application; ranking, by the computing device, the transactions using predefined rules; selecting, by the computing device, a candidate transaction from the ranked list; determining, by the computing device, lines of application code of the monolithic application associated with the candidate transaction; mapping, by the computing device, respective ones of the determined lines of application code to a first operation and a second operation, wherein the first operation and the second operation are different types of operation; and defining, by the computing device, a target state design based on CQRS (Command Query Responsibility Segregation), the target state design including a first microservice that performs the first operation and a second microservice that performs the second operation.

Abstract: The approach is for cognitively transferring IoT configurations between similar IoT ecosystems based on several factors. The approach scans IoT devices from a first and a second IoT ecosystem and extracts user data from the first and the second IoT ecosystem. The approach compares the first IoT ecosystem against the second IoT ecosystem and determines if the first IoT ecosystem is similar to the second IoT ecosystem. If the first and second IoT ecosystem are similar, then transfer data from the first IoT ecosystem to the second IoT ecosystem.

Abstract: A failure rate model modeling a failure rate of a training functionality deployment in a training set of functionality deployments is constructed. The failure rate model is configured to receive functionality deployment data and output a corresponding failure rate prediction. Using the failure rate model, a set of functionality deployment failure rates is predicted, a functionality deployment failure rate in the set of functionality deployment failure rates corresponding to an upcoming functionality deployment. Using the set of functionality deployment failure rates, a deployment sequence of the set of upcoming functionality deployments is constructed to minimize a predicted overall failure rate of the set of upcoming functionality deployments. The deployment of each functionality deployment in the set of upcoming functionality deployments is caused, the deployment comprising activating the upcoming functionality program code according to the deployment sequence.

Abstract: Methods and systems for metrics for energy saving and response behavior are disclosed. A method includes: receiving, by a computing device, for each of a plurality of energy users, consumption time series data from a smart meter of the energy user; determining, by the computing device, for each of the plurality of energy users, demographic data of the energy user; clustering, by the computing device, the energy users based on the consumption time series data and the demographic data; identifying, by the computing device, a plurality of groups of energy users based upon the clustering; and determining, by the computing device, an energy saving program to associate with each of the plurality of groups.

Abstract: A failure rate model modeling a failure rate of a training functionality deployment in a training set of functionality deployments is constructed. The failure rate model is configured to receive functionality deployment data and output a corresponding failure rate prediction. Using the failure rate model, a set of functionality deployment failure rates is predicted, a functionality deployment failure rate in the set of functionality deployment failure rates corresponding to an upcoming functionality deployment. Using the set of functionality deployment failure rates, a deployment sequence of the set of upcoming functionality deployments is constructed to minimize a predicted overall failure rate of the set of upcoming functionality deployments. The deployment of each functionality deployment in the set of upcoming functionality deployments is caused, the deployment comprising activating the upcoming functionality program code according to the deployment sequence.

Abstract: A computer-implemented method includes: receiving, by a computing device, information regarding an application programming interface (API); classifying, by the computing device, the API using natural language classification on to a baseline taxonomy; extracting, by the computing device, information regarding features of the API based on the classifying to the baseline taxonomy; performing, by the computing device, an assessment on the API relative to other APIs within a same class as the API based on the extracting the information regarding the API features and the classifying the API; and updating, by the computing device, the API based on the performing the assessment.

Abstract: The approach is for cognitively transferring IoT configurations between similar IoT ecosystems based on several factors. The approach scans IoT devices from a first and a second IoT ecosystem and extracts user data from the first and the second IoT ecosystem. The approach compares the first IoT ecosystem against the second IoT ecosystem and determines if the first IoT ecosystem is similar to the second IoT ecosystem. If the first and second IoT ecosystem are similar, then transfer data from the first IoT ecosystem to the second IoT ecosystem.

Abstract: Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: assigning resources of a K-tier resource pool to a certain job residing in a job queue, wherein the certain job residing in the job queue features job coupling characterized by an independent job and a dependent job which for completion depends on an output of the independent job, wherein the K-tier resource pool includes at least one foreground virtual machine (VM) having a first central processing unit (CPU) priority and at least one background virtual machine (VM) having a second CPU priority, wherein the first CPU priority is higher than the second CPU priority, wherein the assigning resources of the K-tier resource pool to the certain job includes assigning one or more foreground VM to the independent job and assigning one or more background VM to the dependent job.

Abstract: A computer-implemented method of managing energy supply agreements that includes: connecting to a permissioned blockchain, the blockchain being accessible only by energy suppliers, energy transmission companies and government agencies that regulate a market for the supply of energy by energy suppliers to energy transmission companies; creating a new block for an energy purchase agreement that provides for the purchase of energy by an energy transmission company from an energy supplier; appending the new block having the energy purchase agreement to the blockchain; and responsive to the energy supplier supplying a quantity of energy to the energy transmission company in an individual energy transaction, appending the individual energy transaction as a block to the blockchain.

Abstract: A method collects from at least one power measuring device of an environment power consumption data indicating active power consumption during a timeframe by an electrical device in the environment. The method also collects operating parameter data indicating at least one operating parameter under which the electrical device operates during at least a portion of the timeframe. The method performs, based on observing an increase in power consumption of the electrical device during the timeframe, assessing extents of contribution by potential contributing factors to the increase in power consumption, the potential contributing factors including time-based degradation of the electrical device and the at least one operating parameter. The method outputs, based on the assessing, an indication of an extent of contribution of degradation of the electrical device to the increase in power consumption.

Abstract: Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: assigning resources of a K-tier resource pool to a certain job residing in a job queue, wherein the certain job residing in the job queue features job coupling characterized by an independent job and a dependent job which for completion depends on an output of the independent job, wherein the K-tier resource pool includes at least one foreground virtual machine (VM) having a first central processing unit (CPU) priority and at least one background virtual machine (VM) having a second CPU priority, wherein the first CPU priority is higher than the second CPU priority, wherein the assigning resources of the K-tier resource pool to the certain job includes assigning one or more foreground VM to the independent job and assigning one or more background VM to the dependent job.

Abstract: Systems and methods for monitoring of utility assets using crowd-sourced digital image data are disclosed. In embodiments, a method includes: monitoring, by a computing device, incoming real-time utility data; determining, by the computing device, that a triggering event has occurred based on the monitoring; sending, by the computing device, an event message to one or more agent devices comprising a request for digital image collection at a location based on the triggering event, the event message including event information, wherein the one or more agent devices are associated with respective agents in a crowd-sourcing network; receiving, by the computing device, event data from the one or more agent devices, the event data including one or more digital images; and processing, by the computing device, the one or more digital images to determine next steps with respect to the triggering event.

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: A method, system and computer program product for determining API pricing. Consumption parameters are identified using a supervised learning model. The API consumption parameters refer to any parameters that can be used to describe an API (functionality or otherwise) and can be used to compare other comparable APIs in similar domains provided by other providers. Furthermore, reference pricing is determined using machine learning using the identified API consumption parameters. Additionally, the API price is determined dynamically using the identified API consumption parameters and the determined reference pricing. An API pricing score is then derived for the API price using the supervised learning model. The API price is selected as the suggested price for the API in response to the API pricing score exceeding a threshold value. In this manner, an API price is established that reflects the true value of the API assessed by the API consumer.

Abstract: A method (and structure and computer product) of recognizing and tracking an object includes receiving image data from at least two camera sensors and analyzing the image data to determine attributes of an object detected as present in the image data. A first decision tree (DT) is developed to classify the object, based on the attributes determined from the image data. A second DT is developed to refine the first DT, wherein the second DT is used to refine the first DT by determining which attributes are most significant in the classification of the object.

Abstract: A method, system and computer program product are disclosed for creating a dynamic heterogeneous IoT network from existing IoT network devices to identify and implement a dynamic response to an event. In an embodiment, the method comprises identifying an occurrence of a specified event; and creating a heterogeneous IoT network to respond to the event, including selecting a master IoT device and identifying one or more participatory IoT devices for the created network from multiple IoT devices from one or more existing IoT networks. The master IoT device is in bidirectional communications with the participatory IoT devices. The master device identifies a response to the specified event, translates the response into commands, and sends the commands to the participatory IoT devices to implement the response. The master IoT device may pass control to another master device in another heterogeneous IoT network if the response is not accomplished.

Abstract: A method for transferring digital data from a source to a target device, each of the source and target devices including a respective user interface. The method receives a user selection of digital data on the source device via a user interface. The method authenticates the user on the source device. The method, based on recognizing a user selection of target input field(s), of an interface of the target device, to which the digital data is to be provided as input, authenticates the user on the target device and verifies that a common user has authenticated with the source device and the target device. The method transfers the digital data to the target input field(s) of the interface of the target device.