ETALIS at RR 2010

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The talk about "ETALIS Language for Events", given at the RR 2010 conference.

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ETALIS at RR 2010

  1. 1. A Rule-Based Language for ComplexEvent Processing and ReasoningDarko Anicic, Paul Fodor, Sebastian Rudolph, Roland Stühmer,Nenad Stojanovic, Rudi StuderThe 4th International Conference on Web Reasoning and Rule Systems,Bressanone/Brixen, Italy
  2. 2. Agenda Introduction, Motivation ETALIS Language for Events • Syntax; • Semantics; • Experimental Results; Conclusion.
  3. 3. Complex Event Processing How to capture events from event sources; and transform, combine, interpret and consume them? Figure source: Opher Etzion & Tali Yatzkar, IBM Research • Financial services (high frequency trading); • Sensor networks (wireless and mobile networks); • Real-Time Web (click stream analysis, processing updates from social Web apps, on-line advertising).
  4. 4. Motivation Efficient Complex Event Processing (timeliness & large volume of data are dimensions of concern) J. Agrawal, Y. Diao, D. Gyllstrom, and N. Immerman. Efficient pattern matching over event streams. In SIGMOD (2008). Y. Mei and S. Madden. Zstream: a cost-based query processor for adaptively detecting composite events. In SIGMOD (2010) A. J. Demers, J. Gehrke. Cayuga: A general purpose event monitoring system. In CIDR, 2007. N. H. Gehani, Narain H. Composite event specification in active databases: Model & implementation. In VLDB, 1992.Current CEP systems, based on DSMS provide on the-fly analysis ofdata streams, but cannot combine streams with evolving knowledge, andthey cannot perform reasoning tasks.
  5. 5. Motivation Formal & knowledge-based processing of events (detect events, context, situation and reason about them)F. Bry and M. Eckert. Rule-based composite event queries: The languageXChangeEQ and its semantics (2007);A. Paschke, A. Kozlenkov, and H. Boley. A homogenous reaction ruleslanguage for complex event processing (2007);G. Lausen, B. Ludäscher, W. May. On Active Deductive Databases: TheStatelog Approach (1996);E. Behrends, O. Fritzen, W. May, and F. Schenk. Combining ECA rules withprocess algebras for the Semantic Web. (2006);Incremental Reasoning on Streams and Rich Background Knowledge, D. F.Barbieri, D. Braga, S. Ceri, E. Della Valle, and M. Grossniklaus (2010).Detection of complex events based on an event-driven and event-at-a-time (incremental) evaluation remains a challenge.
  6. 6. Our Approach ETALIS Efficient CEP w.r.t timeliness Knowledge- based based on: Event- and data processing of volumedriven Backwardevents Chaining Rules• ETALIS is an inference system for Complex Event Processing;• Formal semantics to guarantee well defined behaviour;• Deductive rules to express complex relationships between events, matching certain temporal, relational or causal conditions;• Reasoning over streaming events w.r.t contextual (background) knowledge, a current state etc.;• ETALIS suitable for: enrichment of events with background information; detection of more complex situations and intelligent recommendations in real-time; or for accomplishing complex event classifications, clustering, and filtering.
  7. 7. Agenda Introduction, Motivation ETALIS Language for Events • Syntax; • Semantics; • Experimental Results; Conclusion.
  8. 8. ETALIS: Language SyntaxETALIS Language for Events is formally defined by:• pr - a predicate name with arity n;• t(i) - denote terms;• t - is a term of type boolean;• q - is a nonnegative rational number;• 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
  9. 9. ETALIS: Interval-based Semantics
  10. 10. ETALIS: Declarative Semantics
  11. 11. ETALIS: Declarative Semantics
  12. 12. ETALIS: Declarative Semantics
  13. 13. ETALIS: Operational Semantics (SEQ) 1. Complex pattern (nota SEQ b SEQ c → ce1 event-driven rule)((a SEQ b) SEQ c) → ce1 2. Decouplinga SEQ b → ie1 3. Binarizationie1 SEQ c → ce1 4. Event-driven backward chaining rules action action action 1 3 action action action 2 1 2 3 ⊗ e2⊗ e3⊗ e1 e1⊗ e2⊗ e3⊗ e5 ⊗ e4 ⊗ a b c d a b c d
  14. 14. Evaluation Tests I Test patterns: Intel Core Quad CPU Q9400 2,66GHz, 8GB of RAM, Vista x64; ETALIS on SWI Prolog 5.6.64 and YAP Prolog 5.1.3 vs. Esper 3.3.0 Throughput vs. Predicate Selectivity (Sequence) Throughput vs. Stream Size (Sequence) Esper 3.3.0 P-SWI P-YAP Throughput vs. Workload Change (Sequence) 500 Esper 3.3.0 P-SWI P-YAP Esper 3.3.0 P-SWI P-YAP 450 Throughput (1000 x Events/Sec)Throughput (1000 x Events/Sec) 35 30 400 Throughput (1000 x 30 350 25 Events/Sec) 25 300 20 20 250 15 200 15 10 150 10 100 5 5 50 0 0 25 50 75 100 0 25 50 75 100 10% 50% 100% Event stream size x 1000 Event stream size x 1000 Predicate selectivity Figure 3: Sequence - (a) Throughput (b) Throughput vs. Predicate Selectivity (c) Throughput vs. Workload Change
  15. 15. Evaluation Tests II Throughput vs. Negation Selectivity Throughput vs. Workload Change (Negation) Throughput vs. Workload Change (Conjunction) Esper 3.3.0 P-SWI P-Yap Esper 3.3.0 Etalis - SWI Etalis - Yap Esper 3.3.0 P-SWI P-Yap 50 40 45 Throughput (1000 xThroughput (1000 x 45 Throughput (1000 x 40 35 40 Events/Sec) Events/Sec) 35 30 Events/Sec) 35 30 25 30 25 25 20 20 20 15 15 15 10 10 10 5 5 5 0 0 0 10% 50% 100% 25K 50K 75K 100K 25K 50K 75K 100K Selectivity of negated events Event stream size Event stream size Figure 4: Negation - (a) Throughput vs. Selectivity (b) Throughput vs. Workload Change (c) Conjunction - Throughput
  16. 16. Evaluation Tests III Computation Sharing (Sequence) Throughput vs. Workload Change (Disjunction) Throughput for Transitive Closure Esper 3.3.0 P-SWI P-Yap Esper 3.3.0 P-SWI P-Yap Throughput (100 x Events/Sec) Esper 3.3.0 P-SWI P-Yap 30 100 30 Throughput (1000 x 25 25 80Throughput (1000 x Events/Sec) 20 20 Events/Sec) 60 15 15 40 10 10 20 5 5 0 0 0 25K 50K 75K 100K 2500 5000 7500 10000 1 8 16 Event stream size Event stream size Number of queries Figure 5: (a) Disjunction-Throughput (b) Transitive Closure (c) Plan Sharing
  17. 17. Agenda Introduction, Motivation ETALIS Language for Events • Syntax; • Semantics; • Experimental Results; Conclusion.
  18. 18. Conclusion: A Common Framework for Event Processing in ETALIS ETALIS A Reasoning Integration deductive over A general with the rule streaming and domain framework data to extensible knowledge with event- detect framework and at-time complex for CEP databases processing situations ETALIS: A Common Framework for Event Processing18
  19. 19. Thank you! Questions… ETALIS Open source: http://code.google.com/p/etalis See also our live DEMO at:http://krake26.perimeter.fzi.de:8080/etalis Darko.Anicic@fzi.de

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