2026-09-18T20:28:11Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/977632026-06-16T18:55:17Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Samuel Madden.
Wu, Eugene, Ph. D. Massachusetts Institute of Technology
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2015-07-17T19:12:36Z
2015-07-17T19:12:36Z
2015
2015
http://hdl.handle.net/1721.1/97763
912404789
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 171-179).
Data-driven decision making and data analysis has grown in both importance and availability in the past decade, and has seen increasing acceptance in the broader population. Visual tools are needed to help non-technical users explore and make sense of their datasets. However even with existing tools, many common data analysis tasks are still performed using manual, error-prone methods, or simply inaccessible due to non-intuitive interfaces. In this thesis, we addressed a common data analysis task that is ill-served by existing visual analytical tools. Specifically, although visualization tools are well suited to identify patterns in datasets, they do not help users characterize surprising trends or outliers in the visualization and leave that task to the user. We explored the necessary techniques so users can visually explore datasets, specify outliers in the resulting visualizations, and produce explanations that help explain the systematic sources of the outlier values. To this end, we developed three systems: DBWipes, a browser-based visual exploration tool; Scorpion, a set of algorithms that describes the subset of an outlier's input records that "explain away" the anomalous value; and SubZero, a system to track and retrieve the input records that contributed to output records of a complex workflow. From our experiences, we found that existing visual analysis system designs leave a number of program analysis, performance, and functionalities on the table, and proposed an initial design of a data visualization management system (DVMS) that unifies data processing and visualization and can help address these existing issues.
by Eugene Wu.
Ph.D.
179 pages
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.
Explaining data in visual analytic systems
Thesis
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<Abstract>Data-driven decision making and data analysis has grown in both importance and availability in the past decade, and has seen increasing acceptance in the broader population. Visual tools are needed to help non-technical users explore and make sense of their datasets. However even with existing tools, many common data analysis tasks are still performed using manual, error-prone methods, or simply inaccessible due to non-intuitive interfaces. In this thesis, we addressed a common data analysis task that is ill-served by existing visual analytical tools. Specifically, although visualization tools are well suited to identify patterns in datasets, they do not help users characterize surprising trends or outliers in the visualization and leave that task to the user. We explored the necessary techniques so users can visually explore datasets, specify outliers in the resulting visualizations, and produce explanations that help explain the systematic sources of the outlier values. To this end, we developed three systems: DBWipes, a browser-based visual exploration tool; Scorpion, a set of algorithms that describes the subset of an outlier's input records that "explain away" the anomalous value; and SubZero, a system to track and retrieve the input records that contributed to output records of a complex workflow. From our experiences, we found that existing visual analysis system designs leave a number of program analysis, performance, and functionalities on the table, and proposed an initial design of a data visualization management system (DVMS) that unifies data processing and visualization and can help address these existing issues.</Abstract>
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