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Retractable Complex Event Processing
and Stream Reasoning
Darko Anicic, Sebastian Rudolph, Paul Fodor, Nenad
Stojanovic
RuleML : Proceedings of the 5th International Symposium on Rules,
Barcelona, Spain
Agenda

 Introduction, Motivation

 ETALIS Language for Events
   • Syntax;
   • Semantics;
   • Experimental Results;
 Conclusion.
Logic-based Complex Event
                       Processing in ETALIS
Use background (contextual) knowledge to            CEP with on-the-fly knowledge
explore   semantic   relations   between            evaluation and stream
events, and detect otherwise undetectable           reasoning:
complex situation.
                                                    •   Complex situation based on
  ψ=?
  π ← φ ∨ ¬ ψ.                                          explicit data (events) and
  φ = true.                                             implicit/ explicit knowledge
                                                    •   Classification and filtering
  Event
  Sources                                           •   Context evaluation
                                                    •   Intelligent recommendation
                                       Detected
                                       Situations   •   Predictive analysis

                             CEP




                         Pattern
                         Definitions
Motivation

    Knowledge-based CEP & Stream Reasoning


•   Today’s CEP systems are focused mostly on throughput and
    timeliness;
•   Time critical actions/decisions are supposed to be taken upon detection
    of complex events;
•   These actions additionaly require evaluation of background knowledge;
•   Knowledge captures the domain of interest or context related to
    actions/decisions;
•   The task of reasoning over streaming data (events) and the background
    knowledge constitutes a challenge known as Stream Reasoning.

    Current CEP systems provide on the-fly analysis of data streams, but
    mainly fall short when it comes to combining streams with evolving
    knowledge and performing reasoning tasks.
Motivation

  Non-blocking Event Revision – Transactional Events


• Events in today’s CEP systems are assumed to be immutable and
  therefore always correct;
• In some situations however revisions are required:
     • an event was reported by mistake, but did not happen in reality;
     • an event was triggered and later revoked due to a transaction
       failure.
• As recognised in [Ryvkina et al. ICDE’06], event stream sources may
   issue revision tuples that amend previously issued events.

   Current CEP systems provide on the-fly analysis of data streams, but
   typically don’t take these revision tuples into account and produce
   correct revision outputs.
Related Work
DSMS Approaches for retractions in CEP:
   D. Carney et al. Monitoring streams: a new class of data management
   applications. In VLDB’02
   A. S. Maskey et al. Replay-based approaches to revision processing
   in stream query engines. In SSPS’02.
        based on archives of recent data and replying
        whole recent history is kept archived
    R. S. Barga et al. Consistent streaming through time: A vision for
    event stream processing. In CIDR’07.
        based on blocking, buffering and synchronisation point

Stream Reasoning approaches:
    D. F. Barbieri et al. An execution environment for C-SPARQL queries.
    In EDBT’10.
    A. Bolles et al. Streaming SPARQL – Extending SPARQL to Process
    Data Streams. In ESWC’10.
Agenda

 Introduction, Motivation

 ETALIS: Retractable CEP and Stream Reasoning
   • Syntax;
   • Semantics;
   • Experimental Results;
 Conclusion.
ETALIS: Language Syntax

ETALIS 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
ETALIS: Interval-based Semantics
ETALIS: Formal Semantics
ETALIS: Operational Semantics (SEQ)
                          1. Complex pattern (not
a SEQ b SEQ c → ce1
                          event-driven rule)
((a SEQ b) SEQ c) → ce1   2. Decoupling

a SEQ b → ie              3. Binarization
ie SEQ c → ce1

                          4. Event-driven backward
                          chaining rules
ETALIS: Operational Semantics (rSEQ)
Tests I: CEP with Stream Reasoning




                                                                               Throughput Change




                                   Throughput (1000 x Events/Sec)
                                                                    30



                                                                    20



Intel Core Quad CPU Q9400                                           10
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                                     0
                                                                         100   1000      10000     100000

                                                                               Recursion depth
Tests II: Throughput Comparison



                                        Revision Flag off   Revision Flag on
                                 35
Throughput (1000 x Events/Sec)




                                                                                                                      Revision Flag off   Revision Flag on




                                                                               Throughput (1000 x Events/Sec)
                                 30
                                                                                                                30
                                 25

                                 20
                                                                                                                20
                                 15

                                 10                                                                             10
                                  5

                                  0                                                                             0
                                      SEQ      AND       PAR            OR                                           5%          10%            20%
                                                 Operator                                                              Percentage of revised events


                                       Figure: Throughput (a) Various operaors (b) Negation
Tests III: Stock price change on a real
                                   data set




• Yahoo Finance: IBM stocks                                               with revision    without revision

  from 1962 up to now                                          18
                              Throughput (1000 x events/sec)
                                                               16
• 5% revision tulples                                          14
  introduced                                                   12
                                                               10
                                                                8
                                                                6
                                                                4
                                                                2
                                                                0
                                                                    0%   0.50%    1%       2%     5%          10%
                                                                                 Price Increase
Agenda

 Introduction, Motivation

 ETALIS Language for Events
   • Syntax;
   • Semantics;
   • Experimental Results;
 Conclusion.
Conclusion: A Common Framework for Event
                         Processing in ETALIS




                              ETALIS
                      Integration    Reasoning     Retractable
                A
                        with the         over          CEP -
          deductive
                        domain        streaming          an
              rule
                      knowledge        data and     extensible
          framework
                          and        background     framework
            for CEP
                       databases     knowledge        for CEP


             ETALIS: A Common Framework for Event Processing

17
Thank you! Questions…

          ETALIS

          Open source:
http://code.google.com/p/etalis




                         Darko.Anicic@fzi.de

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ETALIS_RuleML_2011_Retractions

  • 1. Retractable Complex Event Processing and Stream Reasoning Darko Anicic, Sebastian Rudolph, Paul Fodor, Nenad Stojanovic RuleML : Proceedings of the 5th International Symposium on Rules, Barcelona, Spain
  • 2. Agenda  Introduction, Motivation  ETALIS Language for Events • Syntax; • Semantics; • Experimental Results;  Conclusion.
  • 3. Logic-based Complex Event Processing in ETALIS Use background (contextual) knowledge to CEP with on-the-fly knowledge explore semantic relations between evaluation and stream events, and detect otherwise undetectable reasoning: complex situation. • Complex situation based on ψ=? π ← φ ∨ ¬ ψ. explicit data (events) and φ = true. implicit/ explicit knowledge • Classification and filtering Event Sources • Context evaluation • Intelligent recommendation Detected Situations • Predictive analysis CEP Pattern Definitions
  • 4. Motivation Knowledge-based CEP & Stream Reasoning • Today’s CEP systems are focused mostly on throughput and timeliness; • Time critical actions/decisions are supposed to be taken upon detection of complex events; • These actions additionaly require evaluation of background knowledge; • Knowledge captures the domain of interest or context related to actions/decisions; • The task of reasoning over streaming data (events) and the background knowledge constitutes a challenge known as Stream Reasoning. Current CEP systems provide on the-fly analysis of data streams, but mainly fall short when it comes to combining streams with evolving knowledge and performing reasoning tasks.
  • 5. Motivation Non-blocking Event Revision – Transactional Events • Events in today’s CEP systems are assumed to be immutable and therefore always correct; • In some situations however revisions are required: • an event was reported by mistake, but did not happen in reality; • an event was triggered and later revoked due to a transaction failure. • As recognised in [Ryvkina et al. ICDE’06], event stream sources may issue revision tuples that amend previously issued events. Current CEP systems provide on the-fly analysis of data streams, but typically don’t take these revision tuples into account and produce correct revision outputs.
  • 6. Related Work DSMS Approaches for retractions in CEP: D. Carney et al. Monitoring streams: a new class of data management applications. In VLDB’02 A. S. Maskey et al. Replay-based approaches to revision processing in stream query engines. In SSPS’02. based on archives of recent data and replying whole recent history is kept archived R. S. Barga et al. Consistent streaming through time: A vision for event stream processing. In CIDR’07. based on blocking, buffering and synchronisation point Stream Reasoning approaches: D. F. Barbieri et al. An execution environment for C-SPARQL queries. In EDBT’10. A. Bolles et al. Streaming SPARQL – Extending SPARQL to Process Data Streams. In ESWC’10.
  • 7. Agenda  Introduction, Motivation  ETALIS: Retractable CEP and Stream Reasoning • Syntax; • Semantics; • Experimental Results;  Conclusion.
  • 8. ETALIS: Language Syntax ETALIS 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
  • 11. ETALIS: Operational Semantics (SEQ) 1. Complex pattern (not a SEQ b SEQ c → ce1 event-driven rule) ((a SEQ b) SEQ c) → ce1 2. Decoupling a SEQ b → ie 3. Binarization ie SEQ c → ce1 4. Event-driven backward chaining rules
  • 13. Tests I: CEP with Stream Reasoning Throughput Change Throughput (1000 x Events/Sec) 30 20 Intel Core Quad CPU Q9400 10 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 0 100 1000 10000 100000 Recursion depth
  • 14. Tests II: Throughput Comparison Revision Flag off Revision Flag on 35 Throughput (1000 x Events/Sec) Revision Flag off Revision Flag on Throughput (1000 x Events/Sec) 30 30 25 20 20 15 10 10 5 0 0 SEQ AND PAR OR 5% 10% 20% Operator Percentage of revised events Figure: Throughput (a) Various operaors (b) Negation
  • 15. Tests III: Stock price change on a real data set • Yahoo Finance: IBM stocks with revision without revision from 1962 up to now 18 Throughput (1000 x events/sec) 16 • 5% revision tulples 14 introduced 12 10 8 6 4 2 0 0% 0.50% 1% 2% 5% 10% Price Increase
  • 16. Agenda  Introduction, Motivation  ETALIS Language for Events • Syntax; • Semantics; • Experimental Results;  Conclusion.
  • 17. Conclusion: A Common Framework for Event Processing in ETALIS ETALIS Integration Reasoning Retractable A with the over CEP - deductive domain streaming an rule knowledge data and extensible framework and background framework for CEP databases knowledge for CEP ETALIS: A Common Framework for Event Processing 17
  • 18. Thank you! Questions… ETALIS Open source: http://code.google.com/p/etalis Darko.Anicic@fzi.de