This document summarizes research on out-of-order event processing. It describes ETALIS, a system for complex event processing (CEP) that uses logic programming to reason about streaming events. ETALIS transforms CEP rules to handle out-of-order events by adding time constraints. It prunes outdated partial event compositions through pattern-based garbage collection with time-based alarms. Evaluation tests on stock market patterns showed ETALIS can effectively detect complex events in out-of-order streams.

1. Results on Out-of-Order Event Processing Paul Fodor, Darko Anicic, Sebastian Rudolph PADL 2011, Austin, TX AIFB

3. Sensor networks

5. Reasoningover streaming events w.r.t contextual (background) knowledge

6. Operators to express complex relationships between events, matching certain temporal, relational or causal conditions

7. An inference system for Complex Event Processing

8. Data-driven complex event processing

9. Combines detection of complex events and reasoning over states

11. t(i) - denote terms;

12. t - is a term of type boolean;

13. q - is a nonnegative rational number;

14. BIN - is one of the binary operators: SEQ, AND, PAR, OR, EQUALS, MEETS, STARTS, or FINISHES.Event rule is defined as a formula of the following shape: where p is an event pattern containing all variables occurring in

15. ETALIS: Interval-based Semantics

16. ETALIS: Declarative Semantics

17. ETALIS: Operational Semantics Complex pattern (not event-driven rule) a ⊗ b ⊗ c -> ce1 ((a ⊗ b) ⊗ c) -> ce1 a ⊗ b -> ie1 ie1 ⊗ c -> ce1 Binarization a :- while_do(a,1). a(1) :- ins(goal(b,a,ie1)). b :- while_do(b,1). b(1) :- goal(b,a,ie1) ⊗ del(goal(b,a,ie1)) ⊗ ie1. ie1 :- while_do(ie1,1). ie1(1) :- ins(goal(c,ie1,ce1)). c :- while_do(c,1). c(1) :- goal(c,ie1,ce1) ⊗ del(goal(c,ie1,ce1)) ⊗ ce1. ce1 :- action1. Event-driven backward chaining rules

19. Incremental Detection of Composed Events – Compiled rules in Prolog Complex pattern (not event-driven rule) a ⊗ b ⊗ c -> ce1 ((a ⊗ b) ⊗ c) -> ce1 a ⊗ b -> ie1 ie1 ⊗ c -> ce1 Binarization a :- while_do(a,1). a(1) :- ins(goal(b,a,ie1)). b :- while_do(b,1). b(1) :- goal(b,a,ie1) ⊗ del(goal(b,a,ie1)) ⊗ ie1. ie1 :- while_do(ie1,1). ie1(1) :- ins(goal(c,ie1,ce1)). c :- while_do(c,1). c(1) :- goal(c,ie1,ce1) ⊗ del(goal(c,ie1,ce1)) ⊗ ce1. ce1 :- action1. Event-driven backward chaining rules

20. Out-of-order Motivating Example Hedge fund with independent cooperating agents events arrive in an out-of-order Received vs. real order of events missing complex events due to an out-of-order stream, e.g., ce1 false positive complex events due to out-of-order events, e.g., ce2

21. Inference Rule Transformation for Out-of-Order Events

22. Pruning Outdated Events- Pushed Constraints -

23. Pruning outdated events- Pattern-Based Garbage Collection - GC via alarms partial composed events are saved into memory with time stamps the rules are labeled with a rule tag X For a given rule the partial goals can be pruned by alarms based on the time stamps pruning for:

24. Evaluation Tests I Stock market patterns

25. Evaluation Tests II

26. Evaluation Tests III

27. Thank you! Questions… ETALIS Open source: http://code.google.com/p/etalis