1. Fusion Tables

2. Takeaways
Relatively “light” paper on a real-world public facing system
Clearly useful to some people and organizations – many users…
Companion paper in SOCC talks about details of implementation
most are standard adaptations of existing techniques

3. Target Users are Data Enthusiasts
Also called “factivists”: those who know nothing about DBMS
They need to find good data, do meaningful data integration, and tell compelling stories
Allow them to upload, collaborate, visualize data
Also combine them with existing datasets
Need to understand semantics of datasets

4. Goals of Fusion Tables
Goal 1: Easy to use database system integrated with the web
Support common workflows
Easy upload
Sharing
Visualizations
Publishing
Goal 2: Fusion with other datasets; find others and combine with yours

5. Any thoughts about the first goal?
Who are the target users for tools like this? We saw some examples…

6. Any thoughts about the first goal?
Who are the target users for tools like this? We saw some examples…
People who want to store and study small datasets
But need something more powerful than excel
Joins, Selects, aggregates (visualizations)
e.g., Journalists, scientists, governments, non-profits

7. Let’s talk about each of these steps..
Data Acquisition
Primarily work on CSVs
They don’t require a schema in advance
Automatically infer schemas
Is this sufficient in practice?

8. Not really!
Studies state that data acquisition accounts for 80% of the development time and cost in data science
What if data is not in csv, but in JSON, or XML?
How would you clean it then? Thoughts?

9. Other Recent Work
There has been some recent work on cleaning data automatically with humans..
(there’s other work on this as well)

10. Drawbacks?

11. Drawbacks? If you make a mistake, hard to go back.
Requires expertise on the part of users
Can you think of other ways to do this acquisition without programming?

12. Drawbacks? If you make a mistake, hard to go back.
Requires expertise on the part of users
Can you think of other ways to do this acquisition without programming?
Examples?
Highlight regions vertically?
Use semantic knowledge?

13. Sharing Fusion tables supports sharing and collaboration on tables;
What are the issues that come up when multiple users are collaborating on tables?

14. Sharing Fusion tables supports sharing and collaboration on tables;
What are the issues that come up when multiple users are collaborating on tables?
What are the issues that come up when there are visualizations that derive from tables?

15. Sharing Fusion tables supports sharing and collaboration on tables;
What are the issues that come up when multiple users are collaborating on tables?
Need for coordination: conflicts.
What are the issues that come up when there are visualizations that derive from tables?

16. Other issues
What if users make mistakes while collaborating? What can we do then?

17. Our recent work: Datahub: Github for data

18. Eventually… Hopefully
Powerful versioning query language

19. Users of Fusion Tables
Examples…

25. Goals of Fusion Tables
Goal 1: Easy to use database system integrated with the web
Support common workflows
Easy upload
Sharing
Visualizations
Publishing
Goal 2: Fusion with other datasets; find others and combine with yours

27. Fusion Tables Implementation
Simple search and suggestion based on existing data
What other use-cases could you see for web-data integration (i.e., finding data on the web to “mesh” with your data)?

28. Other Usecases Row or column augmentation Join augmentation
Missing value augmentation
Accuracy augmentation

29. For example

31. First source of data Many open data initiatives Governments
Non-profits
Collaborations and academic institutions
E.g., uci repository