1. DataWarp: Building Applications which Make Progress in an Inconsistent World Peter Henderson, Robert John Walters, Stephen Crouch, Qinglai Ni

2. Background Computer systems no longer operate in isolation Data is dispersed Data volume makes errors inevitable Fundamental attitude to data is unchanged

3. Classical view of data

4. DataWarp Inspired by TimeWarp (Where applications work without a consistent notion of the time) A framework to allow applications to work without fully consistent data

5. DataWarp (2) Accept that data errors happen Work with what you have Make assumptions if you have to Be prepared to retract or replace actions

6. DataWarp (3) Keep all data Construct candidate views of data Select a view to use Be prepared to change view

7. DataWarp (4)

8. MQDefence Experimental Defence Scenario Ships on a grid Limited ability to discover information about environment Able to communicate Each side seeks to dominate the gird

9. MQDefence – Playing Sink your enemies What about unidentified ships? Could be allies Could be hostile (and dangerous) Early decision gives an advantage

10. MQDefence – Winning Unidentified ships may be hostile or not If hostile, then firing immediately gives us an advantage Should it identify itself as a friend, missile could be destroyed in flight “Shoot first, ask questions later”

11. MQDefence – Winning In DataWarp terms A pair of possible views for each unidentified ship Ships gain an advantage if they use a view in which unidentified ships are marked as hostile View changes can lead to realisation that an ally has been attacked

12. MQDefence - Results Experiment 1Experiment 2 Standard ships destroyed 130 123 DataWarp ships destroyed by standard ships 24 52 8 43 DataWarp ships destroyed by DataWarp ships 2835 Total 182166

13. Conclusion Modern applications are bound to encounter shortcomings in their data Eliminating these is not a realistic option We offer using a DataWarp outlook towards data as a solution