{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/83821"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/83821","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Entropy and Insight: Exploring how information theory can be used to quantify sensemaking in visual analytics","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. 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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.","abstract_has_math":false,"creators":["Holman, Sidney P."],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Computer Science and Applications","degree_department":"Computer Science","school":null,"contributors":[],"advisors":[],"committee_chairs":["North, Christopher L."],"committee_members":["McCrickard, D. 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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. 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We show that using Shannon’s entropy and self-information values will provide a measure of the complexity reduction that results from an analyst’s actions while sorting information, and further demonstrate that reduced complexity can be linked to the knowledge gained. This work 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. 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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."],"dc:description.abstractgeneral":["With the dramatic increase and continued growth of digital information, developing systems that enables humans to make sense of all the data are more necessary than ever before, but there is currently no one-size-fits-all method for measuring or comparing how well a system helps people gain such insight. Rather than trying to pin down a definition of what insight is, we instead look at complexity reduction—with the intuition that, before we can make sense of complex data, we must somehow simplify it in a meaningful way. We show that using Shannon’s entropy and self-information values will provide a measure of the complexity reduction that results from an analyst’s actions while sorting information, and further demonstrate that reduced complexity can be linked to the knowledge gained. This work 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. 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