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A Framework for Considering Comprehensibility in Modeling

Big Data, Volume 4, Number 2 — Jun 2016
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Comprehensibility in modeling is the ability of stakeholders to understand relevant aspects of the modeling process. In this article, we provide a framework to help guide exploration of the space of comprehensibility challenges. We consider facets organized around key questions: Who is comprehending? Why are they trying to comprehend? Where in the process are they trying to comprehend? How can we help them comprehend? How do we measure their comprehension? With each facet we consider the broad range of options. We discuss why taking a broad view of comprehensibility in modeling is useful in identifying challenges and opportunities for solutions.

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BibTex references

  author       = "Gleicher, Michael",
  title        = "A Framework for Considering Comprehensibility in Modeling",
  journal      = "Big Data",
  number       = "2",
  volume       = "4",
  month        = "Jun",
  year         = "2016",
  note         = "ahead of print",
  keywords     = "data analysis; human-computer interaction; visualization; visual analytics; machine learning; statistical modeling",
  ee           = "http://online.liebertpub.com/doi/abs/10.1089/big.2016.0007",
  doi          = "10.1089/big.2016.0007",
  url          = "http://graphics.cs.wisc.edu/Papers/2016/Gle16"

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