University of Illinois Urbana-Champaign

DataSpread: scaling spreadsheets using relational databases

Venkataraman, Vipul

Content Files
VENKATARAMAN-THESIS-2017.pdf

Permalink

https://hdl.handle.net/2142/97692

Description

Title
DataSpread: scaling spreadsheets using relational databases
Author(s)
Venkataraman, Vipul
Issue Date
2017-04-12
Director of Research (if dissertation) or Advisor (if thesis)
Parameswaran, Aditya
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Date of Ingest
2017-08-10T20:32:52Z
Keyword(s)
  • Spreadsheets
  • Interactivity
  • Data models
Abstract
Spreadsheet software is the tool of choice for ad-hoc tabular data management, manipulation, querying, and visualization with adoption by billions of users. However, spreadsheets are not scalable, unlike database systems. We develop DataSpread, a system that holistically unifies databases and spreadsheets with a goal to work with massive spreadsheets: DataSpread retains all of the advantages of spreadsheets, including ease of use, ad-hoc analysis and visualization capabilities, and a schema-free nature, while also adding the scalability and collaboration abilities of traditional relational databases. We design DataSpread with a spreadsheet front-end and a regular relational database back-end. To integrate spreadsheets and databases, in this thesis, we develop a storage and indexing engine for spreadsheet data. We first formalize and study the problem of representing and manipulating spreadsheet data within a relational database. We demonstrate that identifying the optimal representation is NP-Hard via a reduction from partitioning of rectangles; however, under certain reasonable assumptions, can be solved in PTIME. We develop a collection of mechanisms for representing spreadsheet data, and evaluate these representations on a workload of typical data manipulation operations. We augment our mechanisms with novel positionally-aware indexing structures that further improve performance. DataSpread can scale to billions of cells, returning results for common operations within seconds. Lastly, to motivate our research questions, we perform an extensive survey of spreadsheet use for ad-hoc tabular data management.
Graduation Semester
2017-05
Type of Resource
text
Permalink
http://hdl.handle.net/2142/97692
Copyright and License Information
Copyright 2017 Vipul Venkataraman

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Graduate Dissertations and Theses at Illinois PRIMARY
Graduate Theses and Dissertations at Illinois
Dissertations and Theses - Computer Science
Dissertations and Theses from the Siebel School of Computer Science