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Virginia Tech

Entropy and Insight: Exploring how information theory can be used to quantify sensemaking in visual analytics

Abstract

dc:description.abstract

With the dramatic increase and continued growth of digital information, developing Visual Analytic systems that support human cognition and insight generation are more necessary than ever before, but there is currently no content-agnostic method for measuring or com- paring how well a system facilitates analysis. Researchers in industry and academia are developing advanced tools that offer automated data analysis combined with support for human sense-making; tools for a wide variety of sense-making tasks are freely available. Now, the pressing question is: which tool works best, and for what? We show that using Shannon's entropy and self-information measures will provide a measure of the complexity reduction that results from an analyst's actions while sorting the information. Further, we demonstrate that reduced complexity can be linked to the knowledge gained. This is important, because a metric for objectively evaluating the success of current systems in generating insights would establish a standard that future tools could build on. This work could help guide researchers and developers in making the next generation of analytic tools, and in the age of big data the effect of such tools could potentially impact everyone.

Degree

thesis:*
Name thesis:degree_name
Master of Science
Level thesis:degree_level
masters
Discipline thesis:degree_discipline
Computer Science and Applications
Department dc:contributor.department
Computer Science
Grantor dc:publisher
Virginia Tech
Year dc:date.issued
2018

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Holman, Sidney P.
Chair dc:contributor.committeechair
  • North, Christopher L.
Committee members dc:contributor.committeemember
  • McCrickard, D. Scott
  • Harrison, Steven R.

Subjects

dc:subject × 5

Rights

dc:rights
Statement dc:rights
  • In Copyright

Identifiers

dc:identifier.*
Dc Identifier Other
vt_gsexam:15978
OAI identifier oai:identifier
oai:vtechworks.lib.vt.edu:10919/83821

Chain of custody

source
Harvested from
Virginia Tech
Base URL
vtechworks.lib.vt.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Holman, Sidney P.. Entropy and Insight: Exploring how information theory can be used to quantify sensemaking in visual analytics. masters thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/83821