1. Google Fusion Tables: Web-Centered Data Management and Collaboration Hector Gonzalez, Alon Y. Halevy, Christian S. Jensen, Anno Langen, Jayant Madhavan, Rebecca Shapley, Warren Shen, Jonathan Goldberg-Kidon Google Inc. Proceedings of the 2010 international conference on Management of data (SIGMOD '10)

2. Introduction Cloud, Web, Powerful PC devices How would we design data management functionality for today's connected world?

3. Introduction The design goals of Fusion Tables Functionality support of this design. Other Paper provides architecture and implementation. Google Fusion Tables: Data Management, Integration and Collaboration in the Cloud. Proceedings of the Symposium on Cloud Computing, 2010

4. Design Foundations Replace traditional database management? Applications into the cloud? Underlying Principles? – Small set of guiding principles – pay-as-you-go

5. Design Foundations New Application – Ecologists in the rain forests of Costa Rica – Circle of blue. – Current status of health clinics – The International Coffee Organization – Epidemiologist – Visualize data for senator – MTBGuru – Dairy farm in Brazil, manage in Thailand and California

6. Design Foundations Underlying Principles Provide Seamless Integration with the Web – Public Datasets for search engine – Visualization on Web – Powerful Collaboration Emphasize Ease of Use

7. Design Foundations Underlying Principles Provide Incentives for Sharing Data – loss of attribution – misuse and corruption of their data – others not being able to find the data easily. Facilitate Collaboration – discuss and comment

8. Data Management with Fusion Tables Data Acquisition – Upload file – Ease of use, fewer steps – No schema, type – System specify data of column to the type. – If they so desire, user can specify data types.

9. Data Management with Fusion Tables Data Acquisition – Upload file – Ease of use, fewer steps – No schema, type – System specify data of column to the type. – If they so desire, user can specify data types.

10. Data Management with Fusion Tables Data Sharing and Collaboration – Attribution and export – Search – Sharing and integration – Discussions

11. Data Management with Fusion Tables Data Sharing and Collaboration – Sharing and integration

12. Data Management with Fusion Tables Data Sharing and Collaboration – Discussions

13. Data Manipulation and Visualization Table, Map, Intensity map, Line, Bar, Pie, Scatte, Timeline

14. Data Manipulation and Visualization Table, Map, Intensity map, Line, Bar, Pie, Scatte, Timeline

15. Data Manipulation and Visualization Table, Map, Intensity map, Line, Bar, Pie, Scatte, Timeline

16. Data Manipulation and Visualization Table, Map, Intensity map, Line, Bar, Pie, Scatte, Timeline

17. Data Manipulation and Visualization Table, Map, Intensity map, Line, Bar, Pie, Scatte, Timeline

18. Data Manipulation and Visualization Table, Map, Intensity map, Line, Bar, Pie, Scatte, Timeline

19. Data Manipulation and Visualization HTML snippet

20. Fusion Tables API Platform for data management and collaboration Provide developers to extend the others API for creating, inserting, deleting, and updating rows in a table. Authenticated through pre-existing methods for all Google properties.

21. Related Work Several online database management tools exist – ManyEyes (many-eyes.com) – DabbleDB (dabbledb.com) – Socrata (socrata.com) – Factual (factual.com) Fusion Table – collaboration aspects of data management and handles larger datasets.

22. Conclusions Much larger class of users – manage their data – integrated with their other online activities data owners to publish data on the Web easier for users to discover data Provide – more expressive data modeling – query capabilities – adequate performance on larger datasets.