This document proposes a new approach to querying spreadsheets called QuerySheet. It describes some limitations of existing spreadsheet querying systems like Google Query that require rigid data structures. QuerySheet allows more flexible, natural language queries of spreadsheet data. It uses a model-driven approach to structure the data and deal with issues from denormalized data. A preliminary study found QuerySheet was around 70% faster than Google Query and more intuitive and understandable to users. The document outlines the architecture and concludes QuerySheet allows more powerful, robust and productive spreadsheet querying.

1. Jácome Cunha
João Paulo Fernandes
Jorge Mendes
Rui Pereira
João Saraiva
Querying Model-Driven
Spreadsheets
Universidade do Minho
Braga, Portugal

2. Problem: Querying Spreadsheets

3. Problem: Querying Spreadsheets
• Spreadsheets are generally used to store
information
• Much like database systems
• Have no way to query what data they contain
• Attempts have been made to replicate traditional
querying systems for spreadsheets
• MS-Query
• Google QUERY Function

4. Problem: Querying Spreadsheets
• Highly restrict user’s spreadsheet data
• Requires data to be in a tabular format
• Requires headers to be present (in first
row)
• Column letters ≠ Column labels
• Counter intuitive queries
• Data evolution not supported

5. Proposed Solution: Example

6. Proposed Solution: Example
• Model-Driven spreadsheet representing
orders, clients, and products

7. Proposed Solution: Example
• Spreadsheet data instance

8. Proposed Solution: Example
• Query: How much have we profited from
each client?

9. Proposed Solution: Example
Google QUERY Function :
Denormalization

10. Proposed Solution: Example
• Google Query:
Google QUERY Function :

11. Proposed Solution: Example
Google QUERY Function :
Denormalization

12. Proposed Solution: Example
• Google Query:
Google QUERY Function :

13. Proposed Solution: Example
• Proposed Query:
Proposed Solution:

14. Proposed Solution: Example
Results:
• Query: How much have we profited from
each client?

15. Proposed Solution: Example
Results:
Model Inference:

16. Proposed Solution: Example
Results:
Model Inference:
Spreadsheet data instance:

17. Recap

18. Recap
• Take advantage of the powerful Google query engine while
adding extra functionality
• Allow more humanized queries
• Have ClassSheets supported
• Automatically structure the data into the needed format
• Allowing spreadsheet evolution
• Automatically deal with problems caused by denormalized
data querying

19. Architecture

20. QSArchitecture

21. Conclusions

22. Conclusions
• Incremental Denormalization
• Spreadsheet Synchronization
• Evaluation
Future Work
Accomplished
• Spreadsheet querying is more:
• Humanized
• Understandable
• Robust
• Productive
• More functionality

23. Questions?

24. Preliminary Study
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Question 1 Question 2 Question 3 Question 4
Google QuerySheet
• QuerySheet was found to be approx. ~70% faster than
Google’s QUERY function
Minutes

25. Preliminary Study
• After each question, students were asked to choose which
system they preferred in regards to being:
• Intuitive
• Faster to write
• Easier to write
• Understandable
• Almost all preferred QuerySheet on every aspect