There is a stepwise progression in the way signals from the environment and the system under consideration are extracted and transformed into data, then analyzed and abstracted to form representations (e.g., indications and icons) on the user interface. In physical environments, such as aerospace and process control, many system components and their corresponding data and information are interrelated (e.g., an increase in a chamber’s temperature results in an increase in its pressure). These interrelationships, when presented clearly, allow users to understand relations among system components and how they may affect one another. Organization of these interrelationships by means of an orderly structure provides for the so-called “big picture” that pilots, astronauts, and operators strive for.
This research effort begins with the analysis of operational incidents involving current aerospace systems, where the operators have had difficulties understanding the physical interrelationships that existed among several sub-system indications provided on the displays. Analysis of these incidents highlights some of the limitations in the design of information systems with respect to the organization of information and user understanding of the automation processes.
We then contrast less successful and more successful attempts to achieve simplification and abstraction, integration of information, and nonlinear organization of the display to help viewers better understand the system as a whole. These concepts have been applied to the design of a graphical display for a statistical analysis of pilot-automation interaction, and to the design of an experimental engine display for a research helicopter that integrates information from engine parameters and organizes it in the context of other subsystems. The statistical technique known as canonical correlation analysis has been used to analyze and visualize deviations from expected patterns in pilot-automation interaction. A new approach has been developed for transforming continuous signals and creating a discrete alphabet to foster the detection of anomalies in data streams.
Exploration of several real-world examples and several theoretical approaches to information organization suggests a few preliminary hypotheses to be examined in future work:
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