Reference Modeling: Efficient Information Systems Design Through Reuse of Information Models

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A composite of four attributes — confidentiality, integrity, availability, and accountability — plus aspects of a fifth, usability, all of which have the related issue of their assurance. This is done to permit inclusion of software functionality for which no model s exists, or to enable faster simulation times. Programs may include elements of related work outside of the scope of the discrete projects in the program.

Also called work scope.

Efficient Information Systems Design Through Reuse of Information Models

The failure may occur at a value in excess of the minimum required in the specification, i. The model breadth reflects the system requirements coverage in terms of the degree to which the model must address the functional, interface, performance, and physical requirements, as well as other non-functional requirements, such as reliability, maintainability, and safety. For example, a model that specifies the system interfaces may be fairly abstract and represent only the logical information content, such as aircraft status data; or, it may be much more detailed to support higher fidelity information, such as the encoding of a message in terms of bits, bytes, and signal characteristics.

Fidelity can also refer to the precision of a computational model, such as the time step required for a simulation.

Model quality is often assessed in terms of the adherence of the model to modeling guidelines and the degree to which the model addresses its intended purpose. Typical examples of modeling guidelines include naming conventions, application of appropriate model annotations, proper use of modeling constructs, and applying model reuse considerations.

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Specific guidelines are different for different types of models. For example, the guidelines for developing a geometric model using a computer-aided design tool may include conventions for defining coordinate systems, dimensioning, and tolerances.


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  • The definition of systems engineering has evolved over time. It focuses on defining customer needs and required functionality early in the development cycle, documenting requirements, then proceeding with design synthesis and system validation while considering the complete problem:. Systems engineering integrates all the disciplines and specialty groups into a team effort forming a structured development process that proceeds from concept to production to operation. Systems engineering considers both the business and the technical needs of all customers with the goal of providing a quality product that meets the user needs.

    A risk has a probability of occurrence that is greater than zero but less than one, a consequence of occurrence greater than zero, and a time-frame in the future. Models can capture metrics similar to those captured in a traditional document-based approach to systems engineering, but potentially with more precision given the more accurate nature of models compared to documents. Models may also be used to assess progress in terms of the extent to which the requirements have been satisfied by the design or verified through testing.

    Reference Modeling

    When augmented with productivity metrics, the model can be used to estimate the cost of performing the required systems engineering effort to deliver the system. Moreover, particular reference models are discussed and evaluated.

    Data Models in GIS

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