Proactive computing - presentation in the OMG event processing CoP in capital markets, NYC, October 6, 2010

1. Proactive event-driven computing OMG EP CoP: Event Processing Symposium: Capital markets, NYC, October 6 th , 2010 Dr. Opher Etzion IBM Haifa Research Lab [email_address]

2. Imagine that… Your mortgage backed securities decisions are tuned based on The future effect of location-related events on the risk Your are able to mitigate predicted events that would cause your customer contact center to violate SLA

3. Outline of this talk The proactive event-driven computing idea and its relations to other technologies Some building blocks Some scenarios The IBM Research project and your possible involvement

4. The proactive event-driven computing idea and its relations to other technologies Some building blocks Some scenarios The IBM Research project and your possible involvement

5. In June 2010 we presented six trends for event processing: Going from narrow to wide Going from monolithic to diversified Going from proprietary to standard-based Going from stand-alone to embedded Going from reactive to proactive Going from programmer centered to semi-technical developer “ Event processing – seven Years from now”, Opher Etzion, OMG event processing virtual symposium, June 2010

6. Evolution of computing paradigms

8. The class of problems The system state has a metric associated with it The acceptable states are expressed as range on these metric. The system can anticipate that it is going out of the acceptable states The system finds a way either to get to acceptable state or closer Characteristics Desired functionality

9. Gaps from event processing perspective Event Processing Gap 1: Operational vs. Causality based When cell is added – add to total sales When cell is deleted – delete from total sales When cell is modified – delete the old value and add the new value to total sale Analog: spreadsheet Programming Total Sales = Sum (all sale cells) Gap 2: Time and Determinism Situation happens when detected (or at the end of some time window) Situation will happen within 20-30 minutes There is 0.4 probability of false positive Gap 3: Action:

10. Gaps from BI Perspective Data Warehouse Collect Data Apply (predictive) Analytics/ Optimization Analyze Results Change strategy Set policies Watch Event Anticipate short term operational problem Find best feasible alternative in given timeframe Decide & apply Strategic vs. operational issues Batch vs. time-constrained solutions Proactive

11. The programming model gap

12. The proactive event-driven computing idea and its relations to other technologies Some building blocks Some scenarios The IBM Research project and your possible involvement

13. What do we need in order to make it work? Enhance the Event processing Technology Establish proactive action plan based on causality network Apply machine learning techniques to assist in constructing proactive applications

15. Enhance the event processing technology  ime interval Events occur within an interval, possible in the future Predictive EPA Predicted event with Certainty Measure

16. Enhance the event processing technology Proactive Agents Adding proactive agents, actions and feedback loop as part of the model

17. Establish proactive action plan based on causality network Causality network AI planning techniques Time constrained optimization

19. The proactive event-driven computing idea and its relations to other technologies Some building blocks Some scenarios The IBM Research project and your possible involvement

21. Scenario: proactive management of mortgage-backed securities Location based patterns for real-estate value deterioration Determine affected securities (causality network creates transparency) Proactive Planning system Risk policies Decisions and actions Feedback

22. CRM scenario

23. The proactive event-driven computing idea and its relations to other technologies Some building blocks Some scenarios The IBM Research project and your possible involvement

25. IBM Research Worldwide New in 2010: Brazil