A Relational Metric, Its Application to Domain Analysis, and an Example Analysis and Model of a Remote Sensing Domain
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A Relational Metric, Its Application to Domain Analysis, and an Example Analysis and Model of a Remote Sensing Domain

Filetype[PDF-1.46 MB]


  • English
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    • Creators:
      McGreevy, Michael W.
    • Corporate Creators:
      Ames Research Center
    • Subject/TRT Terms:
    • Publication/ Report Number:
      NASA TM 110358
    • Resource Type:
      Tech Report
    • Corporate Publisher:
      Ames Research Center
    • NTL Classification:
      NTL-AVIATION-AVIATION;NTL-AVIATION-Air Traffic Control;NTL-PLANNING AND POLICY-Management Systems;
    • Abstract:
      An objective and quantitative method has been developed for deriving models of complex and specialized spheres of activity (domains) from domain-generated verbal data. The method was developed for analysis of interview transcripts, incident reports, and other text documents whose original source is people who are knowledgeable about, and participate in, the domain in question. To test the method, it is applied here to a report describing a remote sensing project within the scope of the Earth Observing System (EOS). The method has the potential to improve the designs of domain-related computer systems and software by quickly providing developers with explicit and objective models of the domain in a form which is useful for design. Results of the analysis include a network model of the domain, and an object-oriented relational analysis report which describes the modes and relationships in the network model. Other products include a database of relations in the domain, and in interactive concordance. The analysis method utilizes a newly developed relational metric, a proximity-weighted frequency of co-occurrence. The metric is applied to relations between the most frequently occurring terms (words or multiword entities) in the domain text, and the terms found within the contexts of these terms. Contextual scope is selectable. Because of the discriminating power of the metric, data reduction from the association matrix to the network is simple. In addition to their value for design, the models produced by the method are also useful for understanding the domains themselves. They can, for example, be interpreted as models of presence in the domain.
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