2026-09-20T20:06:34Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/531352022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Paris Sabeti.
Reshef, David N
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2010-03-25T15:05:33Z
2010-03-25T15:05:33Z
2009
2009
http://hdl.handle.net/1721.1/53135
505439511
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.
Includes bibliographical references (leaves 112-113).
The ability to capture, store, and manage massive amounts of data is changing virtually every aspect of science, technology, and medicine. This new 'data age' calls for innovative methods to mine and interact with information. VisuaLyzer is a platform designed to identify and investigate meaningful relationships between variables within large datasets through rapid, dynamic, and intelligent data exploration. VisuaLyzer uses four key steps in its approach: 1. Data management: Enabling rapid and robust loading, managing, combining, and altering of multiple databases using a customized database management system. 2. Exploratory Data Analysis: Applying existing and novel statistics and machine learning algorithms to identify and quantify all potential associations among variables across datasets, in a model-independent manner. 3. Rapid, Dynamic Visualization: Using novel methods for visualizing and understanding trends through intuitive, dynamic, real-time visualizations that allow for the simultaneous analysis of up to ten variables. 4. Intelligent Hypothesis Generation: Using computer-identified correlations, together with human intuition gathered through human interaction with visualizations, to intelligently and automatically generate hypotheses about data. VisuaLyzer's power to simultaneously analyze and visualize massive amounts of data has important applications in the realm of epidemiology, where there are many large complex datasets collected from around the world, and an important need to elicit potential disease-defining factors from within these datasets.
(cont.) Researchers can use VisuaLyzer to identify variables that may directly, or indirectly, influence disease emergence, characteristics, and interactions, representing a fundamental first step toward a new approach to data exploration. As a result, the CDC, the Clinton Foundation, and the Harvard School of Public Health have employed VisuaLyzer as a means of investigating the dynamics of disease transmission.
by David N. Reshef.
M.Eng.
113 leaves
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
copyright. They may be viewed from this source for any purpose, but
reproduction or distribution in any format is prohibited without written
permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
VisuaLyzer : an approach for rapid visualization and analysis of epidemiological data
Approach for rapid visualization and analysis of epidemiological data
Thesis
application/pdf
false
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="7ddadcd9-1386-49b1-940c-803584ab24f3">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Language>eng</Language>
<Title>VisuaLyzer : an approach for rapid visualization and analysis of epidemiological data</Title>
<Subtitle>Approach for rapid visualization and analysis of epidemiological data</Subtitle>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2009</PublicationDate>
<Authors>
<Author>
<DisplayName>Reshef, David N</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://dspace.mit.edu/handle/1721.1/7582</License>
<Keyword>Electrical Engineering and Computer Science.</Keyword>
<Abstract>The ability to capture, store, and manage massive amounts of data is changing virtually every aspect of science, technology, and medicine. This new 'data age' calls for innovative methods to mine and interact with information. VisuaLyzer is a platform designed to identify and investigate meaningful relationships between variables within large datasets through rapid, dynamic, and intelligent data exploration. VisuaLyzer uses four key steps in its approach: 1. Data management: Enabling rapid and robust loading, managing, combining, and altering of multiple databases using a customized database management system. 2. Exploratory Data Analysis: Applying existing and novel statistics and machine learning algorithms to identify and quantify all potential associations among variables across datasets, in a model-independent manner. 3. Rapid, Dynamic Visualization: Using novel methods for visualizing and understanding trends through intuitive, dynamic, real-time visualizations that allow for the simultaneous analysis of up to ten variables. 4. Intelligent Hypothesis Generation: Using computer-identified correlations, together with human intuition gathered through human interaction with visualizations, to intelligently and automatically generate hypotheses about data. VisuaLyzer's power to simultaneously analyze and visualize massive amounts of data has important applications in the realm of epidemiology, where there are many large complex datasets collected from around the world, and an important need to elicit potential disease-defining factors from within these datasets.</Abstract>
<Abstract>(cont.) Researchers can use VisuaLyzer to identify variables that may directly, or indirectly, influence disease emergence, characteristics, and interactions, representing a fundamental first step toward a new approach to data exploration. As a result, the CDC, the Clinton Foundation, and the Harvard School of Public Health have employed VisuaLyzer as a means of investigating the dynamics of disease transmission.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>