Abstract: A computer-implemented method assesses an impact of a change management strategy implementation. A first change management index score is identified. A graph theory is used to compute a degree centrality and a betweenness centrality of nodes, based on an adjacency matrix. A change management strategy framework is deployed based on the degree centrality, the betweenness centrality, and a closeness in a social media chain, to transition to one of the nodes having a probability above a predetermined probability threshold of influencing a change from one state to another. Personas are created that are associated with a particular technology, the identified first change management index score, the degree centrality, and the betweenness centrality of the nodes. A Markov chain model transition matrix measures a second change management index score after a time t is measured. A change management strategy framework is altered based on a parametric regression or a polynomial regression.

Abstract: A method includes using, by one or more processors of a computer system, a cognitive model to estimate software development parameters for a software development project based on one or more similar past projects, and automatically assigning, by the one or more processors of the computer system, story points to sprints of the software development project based on the estimated software development parameters.

Abstract: Computer implemented methods, systems, and computer program products include program code executing on a processor(s) that ingests data from one or more computing environments, where the data is related to applications. The processor(s) identifies, based on utilizing topic modeling and latent semantic analysis of the data, homogenous applications among the applications, which include analyzing subdata handled by each application and functionalities of each application; the homogenous applications comprise similarities in the data and in the functionalities. The processor(s) determines overlapping data among the homogenous applications based on the topic modeling, the latent semantic analysis, and term frequency-inverse document frequency of terms in the overlapping data. The processor(s) selects, from the overlapping data, training data. The processor(s) utilizes the training data to calculate weights for disposition metrics and the metrics to predict the resource dispositions for the applications.

Abstract: An end-to-end framework that provides for pair programming can determine a project intent. Programming requirements needed to fulfill the project intent can be identified. The programming requirements can be reduced into lower dimensions. The reduced programming requirements can be clustered into clusters. A common theme can be identified in each of the clusters. From at least one of the clusters having a common theme corresponding to the programming requirements, feasible pairs of developers can be selected. At least one optimal pair of developers can be determined among the feasible pairs using an optimization algorithm that optimizes the project intent.

Abstract: A method for a predictive insulation condition-based recommendation policy includes determining a present insulation level for an insulation layer on a cable providing power to an electronic device based on a current leakage test. The method also includes determining a current leakage and a confidence score for the insulation layer on the cable providing power to the electronic device utilizing a supervised machine learning model. The method also includes generating a decision framework for the electronic device based on the current leakage and the confidence score in response to determining to perform an action based on the decision framework for the electronic device, the method also includes performing the action based on the decision framework to address the current leakage for the insulation layer on the cable providing power to an electronic device.

Abstract: A method includes receiving, by one or more processors of a computer system, historical data related to deployment characteristics and an architecture for past incidents occurring in a data warehouse, predicting, by the one or more processors of the computer system using neural network modeling, tickets related to a response to at least one incident occurring in the data warehouse, wherein the predicting is based on the deployment characteristics and the architecture of the data warehouse, considering, by the one or more processors of a computer system, a plurality of parameters to ascertain the predicted tickets, and providing, by the one or more processors of the computer system, incident ticket volume prediction including the predicted tickets to an incident management system interface reviewable by a user.

Abstract: A method includes using, by one or more processors of a computer system, a cognitive model to estimate software development parameters for a software development project based on one or more similar past projects, and automatically assigning, by the one or more processors of the computer system, story points to sprints of the software development project based on the estimated software development parameters.

Abstract: A method, system, and computer program product for predicting optimal service characteristics to execute predicted IT tickets. The method may include identifying deployment characteristics for an operational system based on an architecture of the operational system. The method may also include receiving implementation data in response to past incident tickets based on the deployment characteristics. The method may also include identifying relationships within the implementation data. The method may also include predicting a volume of new tickets based on the implementation data and the relationships between the implementation data. The method may also include predicting a resolution time for high severity tickets in the volume of new tickets. The method may also include determining preferred service characteristics based on the volume of the new tickets and the resolution time for the high severity tickets.

Abstract: A method, system, and computer program product for predicting optimal service characteristics to execute predicted IT tickets. The method may include identifying deployment characteristics for an operational system based on an architecture of the operational system. The method may also include receiving implementation data in response to past incident tickets based on the deployment characteristics. The method may also include identifying relationships within the implementation data. The method may also include predicting a volume of new tickets based on the implementation data and the relationships between the implementation data. The method may also include predicting a resolution time for high severity tickets in the volume of new tickets. The method may also include determining preferred service characteristics based on the volume of the new tickets and the resolution time for the high severity tickets.

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 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: 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: 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: A method, system and computer program product are disclosed for rewarding customers for sustained energy conservation. In embodiments of the invention, the method comprises establishing a baseline of energy consumption for a customer; defining a rewards program; and determining a reward for the customer, based on the rewards program, for maintaining the customer's energy utilization in a given range compared to the baseline. In embodiments of the invention, the system comprises one or more processing units configured to operate as a file server for receiving specified energy consumption data from customers, and a business analytical engine. The business analytical engine stores a defined rewards program; establishes a baseline of energy consumption for one of the customers; and determines a reward for the one customer, based on the rewards program, for maintaining the one customer's energy utilization in a given range compared to the baseline.

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: An approach is provided for determining an optimal payment instrument. A computer in a system configured in one of four predetermined cloud-based models updates a database with details about account balances and rewards provided by a customer's multiple accounts specifying payment instruments. A request is received from a near field communication enabled mobile device to purchase an item from a retailer. The request includes identifications of the retailer and the item. Based on the identifications, details about the account balances and rewards are retrieved from the database. Based on the details about the account balances and rewards, rules are applied and the optimal payment instrument is determined. A display of the optimal payment instrument to the customer is initiated. The optimal payment instrument is selected and a payment for the item by the optimal payment instrument is initiated.