Christopher De Leon

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2022-67

May 11, 2022

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2022/EECS-2022-67.pdf

Spreadsheets are ubiquitous tools that offer users an intuitive interface for interacting with complex data. However, there are two major problems with modern spreadsheet systems: scalability and usability. Scalability broadly refers to a spreadsheet's ability to remain responsive when dealing with a large dataset, and usability measures how well users can make sense of a spreadsheet. Currently, spreadsheet systems have trouble supporting large datasets that are increasingly common interactively. They also do not provide users with sufficient tooling to better understand the structural layout of their spreadsheets. These limitations can make it much more difficult for most users to identify errors, keep the spreadsheet organized for other users, and for the system to optimize certain computations. To address these issues, we present TACO (Tabular Locality-Based Compression), a framework for performing more efficient spreadsheet computation, and Sherlock, an interface that leverages the TACO framework to support effective sense making of spreadsheets.

Advisors: Aditya Parameswaran

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BibTeX citation:

@mastersthesis{De Leon:EECS-2022-67,
    Author= {De Leon, Christopher},
    Editor= {Parameswaran, Aditya and Tang, Dixin},
    Title= {TOWARD EFFICIENT SPREADSHEET COMPUTATION AND VISUALIZATION},
    School= {EECS Department, University of California, Berkeley},
    Year= {2022},
    Month= {May},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2022/EECS-2022-67.html},
    Number= {UCB/EECS-2022-67},
    Abstract= {Spreadsheets are ubiquitous tools that offer users an intuitive interface for interacting with complex data. However, there are two major problems with modern spreadsheet systems: scalability and usability. Scalability broadly refers to a spreadsheet's ability to remain responsive when dealing with a large dataset, and usability measures how well users can make sense of a spreadsheet. Currently, spreadsheet systems have trouble supporting large datasets that are increasingly common interactively. They also do not provide users with sufficient tooling to better understand the structural layout of their spreadsheets. These limitations can make it much more difficult for most users to identify errors, keep the spreadsheet organized for other users, and for the system to optimize certain computations. To address these issues, we present TACO (Tabular Locality-Based Compression), a framework for performing more efficient spreadsheet computation, and Sherlock, an interface that leverages the TACO framework to support effective sense making of spreadsheets.},
}

EndNote citation:

%0 Thesis
%A De Leon, Christopher 
%E Parameswaran, Aditya 
%E Tang, Dixin 
%T TOWARD EFFICIENT SPREADSHEET COMPUTATION AND VISUALIZATION
%I EECS Department, University of California, Berkeley
%D 2022
%8 May 11
%@ UCB/EECS-2022-67
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2022/EECS-2022-67.html
%F De Leon:EECS-2022-67