Information filtering using bayesian networks: effective user interfaces for aviation weather data

ABSTRACT

Weather is a complex, dynamic process with tremendous impact on aviation. While pilots often have access to large amounts of aviation weather data, they find it difficult and time-consuming to identify weather hazards, due to the sheer amount and cryptic formatting of the data. To address this challenge, we have developed information filtering concepts based on a unified Bayesian network model, integrating text and graphical weather data in the context of specific mission, equipment and personal profiles. Based on these concepts, we have implemented three applications, all of which were to existing technology. Using one of the applications, the AWARE Preflight system, pilots found significantly more hazards in about half the time compared to using the current technology

REFERENCES

• 1: Horvitz, E., Ruokangas, C. and Srinivas, S. (1992) "A Decision-Theoretic Approach to the Display of Information for Time-Critical Decisions: The Vista Project", Proceedings of SOAR-92, NASA/Johnson Space Center, TX
• 2: Mengshoel, O., and Wilkins, D., "RAVEN: Bayesian Networks for Human-Computer Intelligent Interaction", in Vassiliou, M.S. & Huang, T.S. (eds), Computer-Science Handbook for Displays, Rockwell Scientific Publishers, Thousand Oaks, CA, 2001
• 3: Jakob Nielsen, Usability Engineering, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1993
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INDEX TERMS

Primary Classification:
  D. Software
  D.2 SOFTWARE ENGINEERING
        D.2.13 Reusable Software
            Subjects: Domain engineering

Additional Classification:
  D. Software
  D.2 SOFTWARE ENGINEERING
        D.2.13 Reusable Software
            Subjects: Reuse models

General Terms:
Algorithms, Design, Human Factors

Keywords:
bayesian models, bayesian networks, data filtering, information management, intelligent visualization, situation awareness