DataSpread: scaling spreadsheets using relational databases

Venkataraman, Vipul

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

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

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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