Abstract: A method for determining an allocation of a workload includes identifying an employee in an organization having the workload, associating the employee with an articulation workload metric, and determining the allocation of the workload to the employee according to the articulation workload metric.

Abstract: A computer implemented method of constructing a social network, the method including constructing the social network from a plurality of disparate, heterogenous data sources, wherein at least one of the plurality of disparate, heterogenous data sources includes a user generated data source; identifying a plurality of nodes and linkages; determining attributes of the nodes and linkages based on a plurality of disparate, heterogenous data sources, wherein the plurality of disparate, heterogenous data sources includes a combination of the user generated data source and at least one non-user generated source, wherein the attributes include at least one of a deterministic attribute, a probabilistic attribute, and a dynamic attribute; populating a mathemetical decision-making model based on the plurality of nodes and linkages, and the determined attributes of the plurality of nodes and linkages; determining attributes of the nodes and links at a second point in time; re-populating the mathematical decision-making m

Abstract: A computer-implement method and the associated system of computing resources provides an automated work force management capability that optimizes work assignments for individual workers using work force management techniques in combination with social networking analysis (SNA). The work force management attributes are enhanced using SNA. Bipartite graphing processes are used to match the socially enhance worker attributes with the work requirements. By combining the social networking information with the work force management attributes, work assignments are optimized. This optimization exploits historical social interactions between workers and combines their influence with the skills and other work force attributes of each worker required for job performance.

Abstract: Mathematical means and methods are used within the context of mathematical models of a workforce evolution to address key issues in workforce design and planning. Examples of such mathematical means and methods are (but not limited to) fluid-flow models and diffusion-process models. In each case, these mathematical models characterize the workforce evolution over time as a function of dynamic workforce events, such as new hires, terminations, resignations, retirements, promotions and transfers, and dynamic workforce topology, policy, or scenario, such as the viable paths from one workforce resource state to another workforce resource state.

Abstract: Mathematical means and methods are used within the context of mathematical models of a workforce evolution to address key issues in workforce design and planning. Examples of such mathematical means and methods are (but not limited to) fluid-flow models and diffusion-process models. In each case, these mathematical models characterize the workforce evolution over time as a function of dynamic workforce events, such as new hires, terminations, resignations, retirements, promotions and transfers, and dynamic workforce topology, such as the viable paths from one workforce resource state to another workforce resource state.

Abstract: Healthcare fraud detection is accomplished by mining social relationships and analyzing their patterns based on network data structures. Social networks are constructed which depict referral patterns (from health insurance claim information) and associations (from publicly available connection data) to analyze referral patterns and detect possible fraud, abuse and unnecessary overuse. The fraud and abuse management system supports the various aspects of fraud investigation and management, including prevention, investigation, detection and settlement. Using a unique combination of data mining capabilities and graphical reporting tools, the system can identify potentially fraudulent and abusive behavior before a claim is paid or, retrospectively, analyze providers' past behaviors to flag suspicious patterns.