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Title:Intuitive, interactive, and scalable multi-resolution interfaces for accelerating data exploration
Author(s):Rahman, Sajjadur
Director of Research:Parameswaran, Aditya
Doctoral Committee Chair(s):Parameswaran, Aditya
Doctoral Committee Member(s):Karahalios, Karrie; Hart, John C; Battle, Leilani
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):Interactive Data Analytics
Progressive Visualization
Spreadsheet Benchmarking
Spreasheet Exploration
Abstract:In this era of information explosion, data analysis plays a crucial role in decision making across domains. However, with the availability of increasingly large datasets, data analysts are often faced with two types of scalability challenges—perceptual and interactive scalability. Perceptual scalability stems from the increasing complexity and volume of the underlying data being presented or analyzed in the data analysis tools and systems, due to which analysts often get overwhelmed. Interactive scalability stems from delays in data analysis tools and systems generating actionable insights from large datasets, due to increasing sizes of the datasets. In this dissertation, we specifically focus on how these scalability challenges affect two popular platforms for data analysis: visualization tools and spreadsheet systems, and explore different avenues to improve their effectiveness in the presence of scale. To address scalability challenges for visualization tools, we introduce incrementally improving visualizations, wherein we generate interpretable refinements of visualizations on large datasets interactively and operationalize this idea in a tool called IncVisage. IncVisage generates visualizations with progressively improving resolutions so that users can start exploring the data early and make decisions as soon as possible. To address interactive scalability challenges with spreadsheets, we conduct an in-depth benchmarking study on three popular spreadsheet systems: Microsoft Excel, Google Sheets, and LibreOffice Calc. Specifically, we identify when these systems become non-interactive as the scale of the data increases and whether these systems adopt any optimizations to improve performance. We identify a number of optimization opportunities that may improve the responsiveness of these systems on large datasets. Finally, to address the perceptual scalability challenges with spreadsheets, we develop NOAH, a general-purpose plug-in for spreadsheet systems enabling fast and accurate navigation of large spreadsheet datasets. Using NOAH, users can get a bird’s eye view of the data, with the ability to scroll or seek additional details on demand via a multi-resolution overview.
Issue Date:2020-04-30
Type:Thesis
URI:http://hdl.handle.net/2142/107946
Rights Information:Copyright 2020 Sajjadur Rahman
Date Available in IDEALS:2020-08-26
Date Deposited:2020-05


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