Out-of-Order Event Processing Results
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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 It prunes outdated partial event compositions through pattern-based garbage collection with time-based alarms.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.
![Complex Event Processing How to capture events from event sources; and transform, combine, interpret and consume them? Figure source: OpherEtzion & TaliYatzkar, IBM Research ,[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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Out-of-Order Event Processing Results
- 1. Results on Out-of-Order Event Processing Paul Fodor, Darko Anicic, Sebastian Rudolph PADL 2011, Austin, TX AIFB
- 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
- 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
- 27. Thank you! Questions… ETALIS Open source: http://code.google.com/p/etalis