Search

Identifying information requirements is a well-understood activity, but the practice of converting data into visual form based on these requirements is less defined. The Ecological Interface Design (EID) framework attempts to bridge this design gap by offering a set of visual design principles. While these principles supply high-level goals to be achieved by the display, they do not describe the design process per se. EID case studies tend to report the work domain analysis, the design solution and the relationship between these two artefacts. Unfortunately, the presentation of a final solution does not reveal the rationale for decisions made during the design process. This, coupled with the complexity of the systems involved, can make it difficult to transfer design knowledge to other work domains. Here a methodology is proposed to guide the design of visual interface components that make up an ecological display. A structured approach for matching requirements to visual form based on work domain analysis, task analysis, scale matching, and data transformations is presented. A case study reveals the rationale behind the redesign of a process control health reporting system using this methodology.

This paper describes the integration of cognitive analysis into the early stages of design of a new, large-scale system--a next generation US Navy Surface combatant. Influencing complex system designs in ways cognizant of human-system integration principles requires work products that are timely and tightly coupled to other elements of the design process. Because analyses were conducted simultaneously with the design processes regarding ship functionality and staffing, it was necessary to select and adapt cognitive work analysis methods to fit the demands of a time pressured and information-limited design situation. Interviews were conducted and analyzed based on aspects of an abstraction hierarchy and control task models. An abstraction hierarchy, a series of cross-linked matrices, and a set of decision ladder models were developed to provide a principled mapping between system function decompositions produced by system engineering teams and cognitive tasks, information needs, automation requirements, and concepts for displays. Cross-referencing the matrices supported design traceability and facilitated the integration of cognitive analyses with functional analyses being performed by other design teams. Results fed into design recommendations with respect to level of automation, human roles and initial display prototypes for the ship combat command center. The case study illustrates the utility of cognitive work analysis models (specifically, abstraction hierarchies and decision-ladder models) in the design of large-scale, first-of-a-kind systems, and presents new design artifacts that link concepts used in cognitive analyses to those used in systems engineering for more effective integration within the systems engineering process.

The abstraction hierarchy (AH) is a multileveled representation framework, consisting of physical and functional system models, which has been proposed as a useful framework for developing representations of complex work environments. Despite the fact that the AH is well known and widely cited in the cognitive engineering community, there are surprisingly few examples of its application. Accordingly, the intent of this paper is to provide a concrete example of how the AH can be applied as a knowledge representation framework. A formal instantiation of the AH as the basis for a computer program is presented in the context of a thermal-hydraulic process. This model of the system is complemented by a relatively simple reasoning mechanism which is independent of the information contained in the knowledge representation. This reasoning mechanism uses the AH model, along with qualitative user input about system states, to generate reasoning trajectories for different types of events and problems. Simulation outputs showing how the AH model can provide an effective basis for reasoning under different classes of situations, including challenging faults of various types, are presented. These detailed examples illustrate the various benefits of adopting the AH as a knowledge representation framework, namely: providing sufficient representations to allow reasoning about unanticipated fault and control situations, allowing the use of reasoning mechanisms that are independent of domain information, and having psychological relevance.