Abstract: Operation of a manufacturing system is evaluated through mathematical modeling. The system is modeled via a resource graph defining the physical resources of the system, a process graph defining services performed by the system, and a mapping between the resource graph and process graph. Performance metrics of the system are modeled by simulating performance of the services as a function of the resource graph and the process graph under plural sets of operational parameters. A risk may be identified based on the modeled performance metrics, the risk indicating a change in the performance metrics that exceeds a predetermined threshold. A set of operational parameters associated with an outcome absent the risk may then be identified. A modification to the manufacturing system is determined based on this discovery, enabling the system to be optimized to avoid the risk.

Abstract: A patient's health is modeled through multiple scenarios. A base model incorporates rules that govern a response by a human being to one or more diseases, as well as a relation between health metrics and the diseases. Health attributes of the patient are obtained. From the base model and the health attributes, a patient model is generated. The patient model is modeled under different parameters to generate health metrics. From an analysis of the parameters and the resulting health metrics, occurrence probabilities for each of the sets of parameters are determined. Risks are identified, which indicate a likelihood of the patient transitioning from the initial state to an adverse outcome such as a diseased state. A report provides a diagnosis of the patient and one or more remedies/interventions that are predicted, based on the plural sets of parameters and resulting health metrics, to avoid or prevent an adverse outcome.

Abstract: The dynamic complexity and the operational risk inherent in a system are defined and incorporated into a mathematical model of the system. The mathematical model is emulated to predict the states of instability that can occur within the operation of the system. Dynamic complexity of a service is demonstrated where there is an observed effect where the cause can be multiple and seemingly inter-related effects of a many-to-one or many-to-many relationship. Having assessed the dynamic complexity efficiency and the operational risk index of a service (e.g., a business, process or information technology), these indexes can be employed to emulate all attributes of a service, thereby determining how a service responds in multiple states of operation, the states where the dynamic complexity of a service can occur, optimal dynamic complexity efficiency of a service, and the singularities wherein a service becomes unstable.

Abstract: A patient's health is modeled through multiple scenarios. A base model incorporates rules that govern a response by a human being to one or more diseases, as well as a relation between health metrics and the diseases. Health attributes of the patient are obtained. From the base model and the health attributes, a patient model is generated. The patient model is modeled under different parameters to generate health metrics. From an analysis of the parameters and the resulting health metrics, occurrence probabilities for each of the sets of parameters are determined. Risks are identified, which indicate a likelihood of the patient transitioning from the initial state to an adverse outcome such as a diseased state. A report provides a diagnosis of the patient and one or more remedies/interventions that are predicted, based on the plural sets of parameters and resulting health metrics, to avoid or prevent an adverse outcome.

Abstract: The dynamic complexity and the operational risk inherent in a system are defined and incorporated into a mathematical model of the system. The mathematical model is emulated to predict the states of instability that can occur within the operation of the system. Dynamic complexity of a service is demonstrated where there is an observed effect where the cause can be multiple and seemingly inter-related effects of a many-to-one or many-to-many relationship. Having assessed the dynamic complexity efficiency and the operational risk index of a service (e.g., a business, process or information technology), these indexes can be employed to emulate all attributes of a service, thereby determining how a service responds in multiple states of operation, the states where the dynamic complexity of a service can occur, optimal dynamic complexity efficiency of a service, and the singularities wherein a service becomes unstable.

Abstract: The dynamic complexity and the operational risk inherent in a system are defined and incorporated into a mathematical model of the system. The mathematical model is emulated to predict the states of instability that can occur within the operation of the system. Dynamic complexity of a service is demonstrated where there is an observed effect where the cause can be multiple and seemingly inter-related effects of a many-to-one or many-to-many relationship. Having assessed the dynamic complexity efficiency and the operational risk index of a service (e.g., a business, process or information technology), these indexes can be employed to emulate all attributes of a service, thereby determining how a service responds in multiple states of operation, the states where the dynamic complexity of a service can occur, optimal dynamic complexity efficiency of a service, and the singularities wherein a service becomes unstable.

Abstract: The dynamic complexity and the operational risk inherent in a system are defined and incorporated into a mathematical model of the system. The mathematical model is emulated to predict the states of instability that can occur within the operation of the system. Dynamic complexity of a service is demonstrated where there is an observed effect where the cause can be multiple and seemingly inter-related effects of a many-to-one or many-to-many relationship. Having assessed the dynamic complexity efficiency and the operational risk index of a service (e.g., a business, process or information technology), these indexes can be employed to emulate all attributes of a service, thereby determining how a service responds in multiple states of operation, the states where the dynamic complexity of a service can occur, optimal dynamic complexity efficiency of a service, and the singularities wherein a service becomes unstable.

Abstract: The dynamic complexity and the operational risk inherent in a multi-system complex are defined and incorporated into a mathematical model of the complex. The mathematical model is emulated to predict the states of instability that can occur within the operation of the complex. Dynamic complexity of a service is demonstrated where there is an observed effect where the cause can be multiple and seemingly inter-related effects of a many-to-one or many-to-many relationship. Having assessed the dynamic complexity efficiency and the operational risk index of a service (e.g., a business, process or information technology), these indexes can be employed to emulate all attributes of a service, thereby determining how a service responds in multiple states of operation, the states where the dynamic complexity of a service can occur, optimal dynamic complexity efficiency of a service, and the singularities wherein a service becomes unstable.

Abstract: The dynamic complexity and the operational risk inherent in a system are defined and incorporated into a mathematical model of the system. The mathematical model is emulated to predict the states of instability that can occur within the operation of the system. Dynamic complexity of a service is demonstrated where there is an observed effect where the cause can be multiple and seemingly inter-related effects of a many-to-one or many-to-many relationship. Having assessed the dynamic complexity efficiency and the operational risk index of a service (e.g., a business, process or information technology), these indexes can be employed to emulate all attributes of a service, thereby determining how a service responds in multiple states of operation, the states where the dynamic complexity of a service can occur, optimal dynamic complexity efficiency of a service, and the singularities wherein a service becomes unstable.

Abstract: The dynamic complexity and the operational risk inherent in a system are defined and incorporated into a mathematical model of the system. The mathematical model is emulated to predict the states of instability that can occur within the operation of the system. Dynamic complexity of a service is demonstrated where there is an observed effect where the cause can be multiple and seemingly inter-related effects of a many-to-one or many-to-many relationship. Having assessed the dynamic complexity efficiency and the operational risk index of a service (e.g., a business, process or information technology), these indexes can be employed to emulate all attributes of a service, thereby determining how a service responds in multiple states of operation, the states where the dynamic complexity of a service can occur, optimal dynamic complexity efficiency of a service, and the singularities wherein a service becomes unstable.

Abstract: An automated system and method provides a diagnosis and remedy to a business information system. From an initial model of a proposed system architecture, performance metrics are modeled and compared against a set of user-defined corporate and business requirements. The performance metrics include cost, quality of service and throughput. From these metrics and requirements, a business ephemeris or precalculated table is produced, to cross reference enterprise situation and remedy. From the ephemeris, cases can be generated to comprise characteristics, diagnosis and proposed solutions relating to states of the model or business information system. A matching engine matches a case to a state of the model or information system. The matching case can be employed to report a diagnosis and proposed remedial actions, as well as to implement remedial actions automatically. Off line a mathematical modeling member provides feedback to further update the business ephemeris and case base.

Abstract: An automated system and method is provided for system architects to model enterprise-wide architectures of information systems. From an initial model of a proposed system architecture, performance metrics are modeled and compared against a set of user-defined corporate and business requirements, including cost, quality of service and throughput. For unacceptable metrics, modifications to the system architecture are determined and proposed to the system architect. If accepted, the model of the system architecture is automatically modified and modeled again. Once the modeled performance metrics satisfy the corporate and business requirements, a detailed description of the system architecture derived from the model is output. The model of the system architecture also enables a business ephermeris or precalculated table cross referencing enterprise situation and remedy to be formed.

Abstract: Services provided by a business employing an information system are emulated dynamically within a model information system (IS) architecture. The services may be defined by business or corporate management, establishing business objectives that may be categorized as organizational, functional (e.g., performance of the IS architecture) and nonfunctional (e.g., service quality and costs) elements. An emulated service can include a number of such business objectives. By dynamically emulating functional and non-functional aspects of providing a service, a business and supporting IS architecture can be optimized to deliver the service. A computer-based method and system provides such emulation and models the services and IS architecture of an enterprise.

Abstract: A system and method is provided for predictive modeling of technical and non-technical components in a business infrastructure that implementing one or more business solutions. According to a first aspect of the invention, performance metrics generated from a predictive model of a business infrastructure are translated into enterprise decision or indicators that correspond to the service, performance and financial states of a business enterprise. As a result, non-technical executives can utilize the enterprise decision metrics or indicators to evaluate, support, and monitor the effect of business decisions, for example, with respect to profitability, productivity, growth, and risk of the business. According to a second aspect of the invention, the accuracy of the predictive modeling is improved by mathematically expressing the dynamic characteristics and behavior of each infrastructure component as a result of direct and indirect effects of the infrastructure components impacting one another.

Abstract: An automated system and method provides a diagnosis and remedy to a business information system through a case matching process. From an initial model of a proposed system architecture, performance metrics are modeled and compared against a set of user-defined corporate and business requirements. The performance metrics include cost, quality of service and throughput. From these metrics and requirements, a business ephemeris or precalculated table can be produced, the business ephemeris cross referencing enterprise situation and remedy. From the ephemeris, cases can be generated to comprise characteristics, diagnosis and proposed solutions relating to states of the model or business information system. A matching engine matches a case to a state of the model or information system. The matching case can be employed to report a diagnosis and proposed remedial actions, as well as to implement remedial actions automatically.

Abstract: An automated system and method is provided for system architects to model enterprise-wide architectures of information systems. From an initial model of a proposed system architecture, performance metrics are modeled and compared against a set of user-defined corporate and business requirements. The performance metrics include cost, quality of service and throughput. For unacceptable metrics, modifications to the system architecture are determined and proposed to the system architect. If accepted, the model of the system architecture is automatically modified and modeled again. Once the modeled performance metrics satisfy the corporate and business requirements, a detailed description of the system architecture derived from the model is output. The model of the system architecture also enables a business ephermeris or precalculated table cross referencing enterprise situation and remedy to be formed.

Abstract: Information system designs and prototype implementation generated through one or more design and construction phases are validated utilizing predictive modeling. Design validation ensures that the design satisfies a set of performance requirements at each design phase, while validation of prototype implementation ensures the prototype behaves in conformance to the predictive model.

Abstract: A system and method for improving predictive modeling of an information system is disclosed, including generating dynamic representations of the business solution through predictive modeling and providing automated calibration of a predictive model against predefined performance benchmarks.

Abstract: A method and apparatus for designing an optimal IS architecture of a business entity is disclosed. In different levels of abstraction, a business operations model, an applications model, a physical requirements model and technical infrastructure model are produced. The models in a stepwise manner generate an IS architecture meeting desired performance criteria of the business entity. An optimizer evaluates, in a numerical manner, implementations of the IS architecture and design alternatives such that an optimal IS architecture is determined. Subsequent continual refinement of the IS architecture is achieved by remodeling the physical requirements and the technical infrastructure, and by evaluating through the optimizer the then produced IS architecture.

Abstract: The dynamic complexity and the operational risk inherent in a system are defined and incorporated into a mathematical model of the system. The mathematical model is emulated to predict the states of instability that can occur within the operation of the system. Dynamic complexity of a service is demonstrated where there is an observed effect where the cause can be multiple and seemingly inter-related effects of a many-to-one or many-to-many relationship. Having assessed the dynamic complexity efficiency and the operational risk index of a service (e.g., a business, process or information technology), these indexes can be employed to emulate all attributes of a service, thereby determining how a service responds in multiple states of operation, the states where the dynamic complexity of a service can occur, optimal dynamic complexity efficiency of a service, and the singularities wherein a service becomes unstable.