Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/41894

Reducing Complex Visualizations for Analysis

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Title: Reducing Complex Visualizations for Analysis
Authors: McDaniel, Lucas
Nance, Kara
Keywords: data analysis
data visualization
visualization techniques
Issue Date: 04 Jan 2017
Abstract: Data visualization provides a means to present known information in a format that is easily consumable and does not generally require specialized training. It is also well-suited to aid an analyst in discovering previously unknown information [1]. This is possible because visualization techniques can be used to highlight internal relationships and structures within the data, and present them in a graphical manner. Using visualization during the preliminary analysis phase can provide a pathway to enable an analyst to discover patterns or anomalies within the data that might otherwise go undiscovered as humans have an innate ability to visually identify patterns and anomalies. \ \ Even when an analyst has identified a pattern or anomaly within the data, creating an algorithm that allows for automated detection of other occurrences of the same, or similar, patterns is a non-trivial task. While humans are innately skilled at pattern recognition, computers are not, and patterns that might be obvious for a human to identify might be difficult for a computer to detect even when assisted by a skilled analyst [2]. This paper describes a method of taking a complex visualization, and reducing it into several smaller components in order to facilitate computer analysis of the analyst-identified patterns or anomalies in the data. From there, a detection scheme can be generated through an analyst-supervised data analysis process in order to find more occurrences in a larger dataset.
Pages/Duration: 7 pages
URI/DOI: http://hdl.handle.net/10125/41894
ISBN: 978-0-9981331-0-2
DOI: 10.24251/HICSS.2017.730
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
Appears in Collections:CyberWarfare: Offensive and Defensive Software Technologies Minitrack



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