Abstract: An amount of time needed to fill a job requirement is forecasted. By executing a forecasting algorithm, a numerosity of resumes matching the job requirement during the amount of time is forecasted. Using the numerosity and the amount of time, a risk value is computed corresponding to the job requirement, the risk value being indicative of a probability that the job requirement will go unfulfilled in the amount of time. From a base tuple corresponding to the job requirement, a second tuple is constructed, the second tuple having a distance from the base tuple. In real-time a second risk value is computed corresponding to the second tuple. When the second risk value is less than the risk value, data of the second tuple is presented as a risk minimization option for the job requirement.

Abstract: A network of nodes is constructed from data obtained from a data source of a social medium. A node corresponds to a medical professional. From the data, a likelihood is determined of the node prescribing a product. From the data, for a period, a level of knowledge is computed of the node about the product. A change in the level of knowledge of the node from a previous period is determined. Using a change in a level of knowledge corresponding to each node in the network, an amount of knowledge reinforcement to be applied to each node in the network is computed. A knowledge reinforcement resource to perform knowledge reinforcement at a subset of the nodes is allocated according to a schedule, where the allocated knowledge reinforcement resource to the node has a correspondence with the change in the level of knowledge of the node.

Abstract: A set of factors affecting a sold volume of the product is collected at a store in a geographical portion of a geographical area. A subset of a set of area stores is identified having a corresponding sold volume of the product. An intrinsic capacity of the product is computed by adjusting the sold volume of an area store by a first volume where the first volume is due to a sale of a second product at the area store. A modified intrinsic capacity is computed by modifying an effect of a factor from the subset on the intrinsic capacity such that the effect is worse than an effect of the factor at the store. A virtual store is constructed using modified intrinsic capacities of a sub-subset of area stores. A target volume of the product is set at the store as the virtual intrinsic capacity at the virtual store.

Abstract: Aspects provide for selective location of supplies of goods based on dynamic population density and travel distance metrics. Consumer population density forecasts are determined for different geographic locations as functions of distances to different events having different population amounts and geographic population locations. A geographic maximal density location point is determined between geographic locations of the different events and that is located at different distances from the locations of different events as a function of differences in respective consumer population density forecasts for the events, and closer to an event location with a higher consumer population density forecast relative to the location of another event. A quantity of goods is allocated to a supply site that is located at the maximal density location point in an amount selected to maximize a business value of the goods as a function of a population distribution of the maximal density location point.

Abstract: Computing systems, methods and tools for managing and optimizing the allocation of enterprise resources by automating employee management decision making to align management decisions made on a local level with the goals of the overall enterprise, global economies and emerging market trends. The computing systems, methods and tools use historical data, objective attributes and subjective feedback at the local level to predict employee progression at specific points in time of an employee's career and the effects particular management decisions may have on the employee as an asset of an enterprise, in order to predict, calculate and select the optimal actions that will further improve the employee's value to the enterprise yielding an optimal return on the investment of resources.

Abstract: Selected resources that satisfy a specified technical requirement are entered into a selected pool as a function of a sourcing rate, a screening rate and a selection rate that are each variable over time and applied during a sequence of selection time periods that span a demand forecast time period. Offers for acquisition are iteratively made to the selected pool of resources in a sequence of different offer time periods, wherein the resources are progressively moved into different awaiting offer buffers after each of the offer time periods. The demand number is re-forecast and one or more of the sourcing, screening and selection rates adjusted to minimize a combination of costs of the rates with a cost of acquiring resources to satisfy the gap number as a function of the demand number.

Abstract: From a log of a machine, an entry is selected relating to providing a subservice in processing a service request from a requestor associated with a key. The log entry includes a subsequence of machines used and a cost of providing the subservice. A set of entries is selected from the log, an entry including the subsequence and a second cost of providing the subservice but in processing a different service request from a different requestor associated with a different key. A distance is computed between the cost and the second cost. A number of occurrences of the subsequence with the key is determined. Using the number and the distance for the subsequence, a value pair is computed. Responsive to an aggregate number in the value pair not exceeding a threshold count. The processing of the service request is output as a suspect for using an improper sequence of machines.

Abstract: A present risk aversion of a customer is determined. A temporal preference is detected using historical data related to the customer, the temporal preference showing a preference of current utility over a future utility of a product. Using the temporal preference and a negative transaction risk, a future risk aversion of the customer is projected at a future time. A pattern of offer acceptance by the customer is identified in the historical data. A value of an exogenous factor is determined on which a buying ability of the customer depends. The customer is classified in a cluster, where all customers in the cluster have the present risk aversion, the temporal preference, the negative transaction risk, the future risk aversion, and the value. An offer for a product is presented to the cluster, which satisfies the present risk aversion, and where a probability of acceptance of the offer exceeds a threshold.

Abstract: Aspects model a set of different employee compensation adjustment factors from a locally weighted linear regression function of employee data. Retention probabilities are generated for retaining each of the employees, and employee retention costs modeled as a function of historic employee wage data, the modeled set of employee compensation adjustment factors and the retention probabilities. Costs are modeled for replacing employees as a function of the employee wage data and the historic market data, and revenues are modeled for employee productivity as a function of the retention probabilities and the historic business performance and strategy data. The modeled employee compensation adjustment factors are iteratively optimized to maximize a profit objective value determined as a function of the modeled costs for replacing employees and employee productivity revenues.