Information Theoretic Measures for Visual Analytics: The Silver Ticket?
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
In the context of text-based analysis workflows, we propose that an effective analytic tool facilitates triage by a) enabling users to identify and set aside irrelevant content (i.e., reduce the complexity of information in a dataset) and b) develop a working mental model of which items are most relevant to the question at hand. This LDRD funded research developed a dataset that is enabling this team to evaluate propose normalized compression distance (NCD) as a task, user, and context-insensitive measure of categorization outcomes (Shannon entropy is reduced as order is imposed). Effective analytics tools help people impose order, reducing complexity in measurable ways. Our concept and research was documented in a paper accepted to the ACM conference "Beyond Time and Error: Novel Methods in Information Visualization Evaluation", part of the IEEE VisWeek Conference, Baltimore, MD, October 16-21, 2016. The paper is included as an appendix to this report.
- Research Organization:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA); USDOE Laboratory Directed Research and Development (LDRD) Program
- DOE Contract Number:
- AC04-94AL85000
- OSTI ID:
- 1563074
- Report Number(s):
- SAND-2016-10412; 648340
- Country of Publication:
- United States
- Language:
- English
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