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EP-SPARQL: A Unified Language for EventProcessing and Stream Reasoning
Darko Anicic1 Paul Fodor2 Sebastian Rudolph3
Nenad Stojanovic1
1FZI Research Center for Information Technology, Karlsruhe, Germany
2State University of New York at Stony Brook, USA
3Karlsruhe Institute of Technology, Karlsruhe, Germany
WWWW 2011, Hyderabad, India
IntroductionEP-SPARQL
Experimental ResultsConclusion
MotivationRelated Work
Introduction
Real-time data appear increasingly today everywhere.How to effectively process this data?
Financial sectorto find dealing opportunities across available assetsto detect fraud and enable real-time surveillanceto monitor operational risks
Traffic control systemsto observe traffic-update events and (re)plan the trafficto route the traffic and optimise paths
Sensor networksto process sensor data and detect real-time observationse.g., weather observations like tsunamis, hurricanes etc.
D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
IntroductionEP-SPARQL
Experimental ResultsConclusion
MotivationRelated Work
Introduction
Real-time data appear increasingly today everywhere.How to effectively process this data?
Financial sectorto find dealing opportunities across available assetsto detect fraud and enable real-time surveillanceto monitor operational risks
Traffic control systemsto observe traffic-update events and (re)plan the trafficto route the traffic and optimise paths
Sensor networksto process sensor data and detect real-time observationse.g., weather observations like tsunamis, hurricanes etc.
D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
IntroductionEP-SPARQL
Experimental ResultsConclusion
MotivationRelated Work
Introduction
Real-time data appear increasingly today everywhere.How to effectively process this data?
Financial sectorto find dealing opportunities across available assetsto detect fraud and enable real-time surveillanceto monitor operational risks
Traffic control systemsto observe traffic-update events and (re)plan the trafficto route the traffic and optimise paths
Sensor networksto process sensor data and detect real-time observationse.g., weather observations like tsunamis, hurricanes etc.
D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
IntroductionEP-SPARQL
Experimental ResultsConclusion
MotivationRelated Work
Introduction
Real-time data appear increasingly today everywhere.How to effectively process this data?
Financial sectorto find dealing opportunities across available assetsto detect fraud and enable real-time surveillanceto monitor operational risks
Traffic control systemsto observe traffic-update events and (re)plan the trafficto route the traffic and optimise paths
Sensor networksto process sensor data and detect real-time observationse.g., weather observations like tsunamis, hurricanes etc.
D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
IntroductionEP-SPARQL
Experimental ResultsConclusion
MotivationRelated Work
Supporting Real-Time Information Processing
An event is something that occurs, happens or changesthe current state of affairs.To detect more complex dynamic matters, (simpler) eventsare combined into complex events.
Event Processingdeals with the task of processing events with the goal ofidentifying meaningful situations, using event operators as wellas temporal and semantic relationships.
D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
IntroductionEP-SPARQL
Experimental ResultsConclusion
MotivationRelated Work
Supporting Real-Time Information Processing
An event is something that occurs, happens or changesthe current state of affairs.To detect more complex dynamic matters, (simpler) eventsare combined into complex events.
Event Processingdeals with the task of processing events with the goal ofidentifying meaningful situations, using event operators as wellas temporal and semantic relationships.
D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
IntroductionEP-SPARQL
Experimental ResultsConclusion
MotivationRelated Work
Supporting Real-Time Knowledge Processing
Current EP systems provide on-the-fly analysis of datastreams, but fall short of combining events with higher-levelbackground knowledge.Background knowledge describes the context or domain inwhich events are interpreted.Reasoning techniques are necessary for handlingbackground knowledge as events occur!
Stream Reasoningdeals with the task of conjunctively reasoning over streamingdata and background knowledge.
D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
IntroductionEP-SPARQL
Experimental ResultsConclusion
MotivationRelated Work
Supporting Real-Time Knowledge Processing
Current EP systems provide on-the-fly analysis of datastreams, but fall short of combining events with higher-levelbackground knowledge.Background knowledge describes the context or domain inwhich events are interpreted.Reasoning techniques are necessary for handlingbackground knowledge as events occur!
Stream Reasoningdeals with the task of conjunctively reasoning over streamingdata and background knowledge.
D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
IntroductionEP-SPARQL
Experimental ResultsConclusion
MotivationRelated Work
Toward Real-Time Semantic Web
Event Processing (EP)
han
dles
Rapidly changing data represented as events
Figure: Evaluation strategies
D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
IntroductionEP-SPARQL
Experimental ResultsConclusion
MotivationRelated Work
Toward Real-Time Semantic Web
Semantic Web technologies including
han
dles
Event Processing (EP)
han
dles
Rapidly changing data represented as events
Static or slowly evolving background knowledge
Figure: Evaluation strategies
D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
IntroductionEP-SPARQL
Experimental ResultsConclusion
MotivationRelated Work
Toward Real-Time Semantic Web
Semantic Web technologies including
han
dles
Event Processing (EP)
han
dles
Rapidly changing data represented as events
EP SPARQL EP-SPARQL
Static or slowly evolving background knowledge
Figure: Evaluation strategies
D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
IntroductionEP-SPARQL
Experimental ResultsConclusion
MotivationRelated Work
Toward Real-Time Semantic Web
Semantic Web technologies including
han
dles
Event Processing (EP)
han
dles
Rapidly changing data represented as events
EP SPARQL EP-SPARQL
Static or slowly evolving background knowledge
• Temporal relatedness • Semantic relatedness • Stream reasoning
Figure: Evaluation strategies
D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
IntroductionEP-SPARQL
Experimental ResultsConclusion
MotivationRelated Work
Related Work
Streaming DatabasesSASE [1]ZStream [10]CEDR [6]TelegraphCQ [5]
Temporal (and Spatial) RDFIntroducing time into RDF [8]SPARQL-ST [11], Temporal SPARQL [12], stSPARQL [9],and T-SPARQL [7]
Stream ReasoningC-SPARQL [3]Streaming Knowledge Bases [13]Streaming SPARQL [4]Incremental reasoning [2]
D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
IntroductionEP-SPARQL
Experimental ResultsConclusion
SyntaxSemanticsExecution Model
Syntax of the Language
EP-SPARQL– extends SPARQL to enable event-based processing thattakes into account temporal situatedness of triple assertions.– syntactical and semantic downward-compatibility to plainSPARQL.
1 Operators: FILTER, AND, UNION, OPTIONAL, SEQ, EQUALS,OPTIONALSEQ, and EQUALSOPTIONAL;
2 getDURATION() yields a literal of type xsd:durationgiving the time interval associated to the graph pattern;
3 getSTARTTIME() and getENDTIME() retrieve the timestamps of type xsd:dateTime of the start and end of theinterval;
D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
IntroductionEP-SPARQL
Experimental ResultsConclusion
SyntaxSemanticsExecution Model
Syntax of the Language
EP-SPARQL– extends SPARQL to enable event-based processing thattakes into account temporal situatedness of triple assertions.– syntactical and semantic downward-compatibility to plainSPARQL.
1 Operators: FILTER, AND, UNION, OPTIONAL, SEQ, EQUALS,OPTIONALSEQ, and EQUALSOPTIONAL;
2 getDURATION() yields a literal of type xsd:durationgiving the time interval associated to the graph pattern;
3 getSTARTTIME() and getENDTIME() retrieve the timestamps of type xsd:dateTime of the start and end of theinterval;
D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
IntroductionEP-SPARQL
Experimental ResultsConclusion
SyntaxSemanticsExecution Model
Informal Semantics
RDF stream – a set of triple occurrences 〈〈s,p,o〉, tα, tω〉 where〈s,p,o〉 is an RDF triple and tα, tω are the start and end of theinterval.
FILTER – restricts variable bindings to those 〈µ, tα, tω〉 forwhich the filter expression evaluates to true;AND – joins 〈µ, tα, tω〉 and 〈µ′, t ′α, t ′ω〉. The joined tuple hastimestamp t ′′α = min(tα, t ′α), t ′′ω = max(tω, t ′ω);UNION – forms the disjunction of 〈µ, tα, tω〉 and 〈µ′, t ′α, t ′ω〉;OPTIONAL – matches 〈µ, tα, tω〉 optionally with 〈µ′, t ′α, t ′ω〉when the filter expression evaluates to true;
D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
IntroductionEP-SPARQL
Experimental ResultsConclusion
SyntaxSemanticsExecution Model
Informal Semantics (cont’d)
SEQ – joins 〈µ, tα, tω〉 and 〈µ′, t ′α, t ′ω〉 only if 〈µ′, t ′α, t ′ω〉occurs strictly after 〈µ, tα, tω〉;EQUALS – joins 〈µ, tα, tω〉 and 〈µ′, t ′α, t ′ω〉 if they occur
simultaneously;OPTIONALSEQ and EQUALSOPTIONAL aretemporal-sensitive variants of OPTIONAL;CONSTRUCT – generates the stream enriched by triplesfrom possibly iterative CONSTRUCT rules. SELECT-queriesget evaluated not against the pure input stream but againstthe enriched generated stream.
D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
IntroductionEP-SPARQL
Experimental ResultsConclusion
SyntaxSemanticsExecution Model
Example I
Continuously search for companies having a larger than 20%stock price increase in less than 15 days without havingacquired another company during that period.
SELECT ?company WHERE{ ?company hasStockprice ?price1 }
SEQ { { ?company hasAcquired ?othercompany }OPTIONALSEQ{ ?company hasStockPrice ?price2 } }
FILTER ( ?price2 > ?price1 * 1.2 &&!BOUND(?othercompany) &&getDURATION() < "P15D"ˆˆxsd:duration)
D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
IntroductionEP-SPARQL
Experimental ResultsConclusion
SyntaxSemanticsExecution Model
Example II
Detect slow traffic and its cause which could be used toautomatically modify a speed limit on a certain roads.
SELECT ?road ?speed WHERE{ ?road tr: slowTrafficDue ?observ }
SEQ {{ ?road tr: slowTrafficDue ?observ }{ ?observ rdfs:subClassOf tr:SlowTraffic }{ ?observ wt:speed ?speed }}
FILTER ( getDURATION() < "P1H"ˆˆxsd:duration)
Observ_1 Observ_2rdf:type tr:GhostDriver ; rdf:type tr:IceConditions ;wt:speed "50"ˆˆxsd:int . wt:speed "40"ˆˆxsd:int .
tr:GhostDriver rdfs:subClassOf tr:SlowTraffic.tr:IceConditions rdfs:subClassOf tr:BadWeather.
D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
IntroductionEP-SPARQL
Experimental ResultsConclusion
SyntaxSemanticsExecution Model
Sequence OperatorSELECT ?company WHERE
{ ?comp hasStockPrice ?pr1 }SEQ { ?comp hasStockPrice ?pr2 }SEQ { ?comp hasStockPrice ?pr3 }
〈〈s, p, o〉, ti , tj 〉 represented as triple(s, p, o,Ti ,Tj ), and τ represents s, p, o.
triple(τi ,T1,T4)← triple(τ1,T1,T2) SEQ triple(τ2,T3,T4).triple(τ,T1,T6)← triple(τi ,T1,T4) SEQ triple(τ3,T5,T6).
Rule transformation – Incremental computation (Prolog syntax)
triple(τ1,T1,T2) :-assert
(goal(triple(τ2, , ), triple(τ1,T1,T2), triple(τi , , ))
).
triple(τ2,T3,T4) : −goal(triple(τ2, , ), triple(τ1,T1,T2), triple(τ, , )),T2 < T3,retract
(goal(triple(τ2, , ), triple(τ1,T1,T2), triple(τi , , ))
),
triple(τi ,T1,T4).
D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
IntroductionEP-SPARQL
Experimental ResultsConclusion
SyntaxSemanticsExecution Model
Sequence OperatorSELECT ?company WHERE
{ ?comp hasStockPrice ?pr1 }SEQ { ?comp hasStockPrice ?pr2 }SEQ { ?comp hasStockPrice ?pr3 }
〈〈s, p, o〉, ti , tj 〉 represented as triple(s, p, o,Ti ,Tj ), and τ represents s, p, o.
triple(τi ,T1,T4)← triple(τ1,T1,T2) SEQ triple(τ2,T3,T4).triple(τ,T1,T6)← triple(τi ,T1,T4) SEQ triple(τ3,T5,T6).
Rule transformation – Incremental computation (Prolog syntax)
triple(τ1,T1,T2) :-assert
(goal(triple(τ2, , ), triple(τ1,T1,T2), triple(τi , , ))
).
triple(τ2,T3,T4) : −goal(triple(τ2, , ), triple(τ1,T1,T2), triple(τ, , )),T2 < T3,retract
(goal(triple(τ2, , ), triple(τ1,T1,T2), triple(τi , , ))
),
triple(τi ,T1,T4).
D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
IntroductionEP-SPARQL
Experimental ResultsConclusion
SyntaxSemanticsExecution Model
Sequence OperatorSELECT ?company WHERE
{ ?comp hasStockPrice ?pr1 }SEQ { ?comp hasStockPrice ?pr2 }SEQ { ?comp hasStockPrice ?pr3 }
〈〈s, p, o〉, ti , tj 〉 represented as triple(s, p, o,Ti ,Tj ), and τ represents s, p, o.
triple(τi ,T1,T4)← triple(τ1,T1,T2) SEQ triple(τ2,T3,T4).triple(τ,T1,T6)← triple(τi ,T1,T4) SEQ triple(τ3,T5,T6).
Rule transformation – Incremental computation (Prolog syntax)
triple(τ1,T1,T2) :-assert
(goal(triple(τ2, , ), triple(τ1,T1,T2), triple(τi , , ))
).
triple(τ2,T3,T4) : −goal(triple(τ2, , ), triple(τ1,T1,T2), triple(τ, , )),T2 < T3,retract
(goal(triple(τ2, , ), triple(τ1,T1,T2), triple(τi , , ))
),
triple(τi ,T1,T4).
D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
IntroductionEP-SPARQL
Experimental ResultsConclusion
SyntaxSemanticsExecution Model
Other Operators
SELECT ?company WHERE...FILTER ( ?price2 < ?price1 * 0.7 &&
?price3 > ?price1 * 1.05)
FILTER – Rule transformation
condition(Price1,Price2,Price3) : −P1 is (Price1 ∗ 0.7), P1>Price2,P2 is (Price1 ∗ 0.5), Price3>P2.
EQUALS – Rule transformation
equals(TI1,TI2) : −TI1 = [TI1 S,TI1 E ], validTimeInterval(TI1),TI2 = [TI2 S,TI2 E ], validTimeInterval(TI2),TI1 S = TI2 S,TI1 E = TI2 E .
validTimeInterval(TI)←TI = [TI S,TI E ],TI S@ < TI E .
D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
IntroductionEP-SPARQL
Experimental ResultsConclusion
SyntaxSemanticsExecution Model
Other Operators
SELECT ?company WHERE...FILTER ( ?price2 < ?price1 * 0.7 &&
?price3 > ?price1 * 1.05)
FILTER – Rule transformation
condition(Price1,Price2,Price3) : −P1 is (Price1 ∗ 0.7), P1>Price2,P2 is (Price1 ∗ 0.5), Price3>P2.
EQUALS – Rule transformation
equals(TI1,TI2) : −TI1 = [TI1 S,TI1 E ], validTimeInterval(TI1),TI2 = [TI2 S,TI2 E ], validTimeInterval(TI2),TI1 S = TI2 S,TI1 E = TI2 E .
validTimeInterval(TI)←TI = [TI S,TI E ],TI S@ < TI E .
D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
IntroductionEP-SPARQL
Experimental ResultsConclusion
SyntaxSemanticsExecution Model
Other Operators
SELECT ?company WHERE...FILTER ( ?price2 < ?price1 * 0.7 &&
?price3 > ?price1 * 1.05)
FILTER – Rule transformation
condition(Price1,Price2,Price3) : −P1 is (Price1 ∗ 0.7), P1>Price2,P2 is (Price1 ∗ 0.5), Price3>P2.
EQUALS – Rule transformation
equals(TI1,TI2) : −TI1 = [TI1 S,TI1 E ], validTimeInterval(TI1),TI2 = [TI2 S,TI2 E ], validTimeInterval(TI2),TI1 S = TI2 S,TI1 E = TI2 E .
validTimeInterval(TI)←TI = [TI S,TI E ],TI S@ < TI E .
D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
IntroductionEP-SPARQL
Experimental ResultsConclusion
Test 1: Event ProcessingTest 2: Stream ReasoningTest 3: Example applications
Event Processing
Test pattern: monitoringthe average stock priceof a company X withCONSTRUCT queriesIntel Core Quad CPUQ9400 2,66GHz, 8GB ofRAM, Ubuntu 9.10ETALIS on SWI Prolog5.6.64 and YAP Prolog5.1.3 vs. Esper 3.3.0
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Figure: Aggregation over countsliding window
D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
IntroductionEP-SPARQL
Experimental ResultsConclusion
Test 1: Event ProcessingTest 2: Stream ReasoningTest 3: Example applications
Stream Reasoning
Test pattern: infer overstreaming tripleswhether the subject of atriple is an instance ofthe class of concern,or any of its 40,080subclasses.
!"#$"%$&'()*+,$-,#$.+/,"0(01$2+*/3
!
"!!
#!!!
#"!!
$!!!
"!!! #!!!! #"!!! $!!!!
!456+'$"%$&'()*+,.+/,"0(01$2+*/3$(0$5,
%&'()*+,'-
Figure: Delay caused by streamreasoning
D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
IntroductionEP-SPARQL
Experimental ResultsConclusion
Test 1: Event ProcessingTest 2: Stream ReasoningTest 3: Example applications
Example application I
Goods Delivery Systemin the city of Milan;RDF knowledge base torepresent locations andtraffic links;The system “listens” totraffic-update events andreschedule paths.
3 94 109 130 31
889
1076
1295
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5 10 15 20
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sum
ed ti
me
in m
s Number of locations
No. of Locations vs. Consumed Time
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35 86 119 165
204
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sum
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emor
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Figure: Delay caused byprocessing
D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
IntroductionEP-SPARQL
Experimental ResultsConclusion
Test 1: Event ProcessingTest 2: Stream ReasoningTest 3: Example applications
Example application II
A tsunami detectionsystem;Real buoy sensor datacontinuously processed;GeoNames to providegeographical placeswithin a certain radiusfrom the sensor location.
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-0.02
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Threshold
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Figure: Tsunami detectionhistogram
D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
IntroductionEP-SPARQL
Experimental ResultsConclusion
ConclusionExampleSemantics
Conclusion
1 Addressing dynamics and notification on the Web hasbecome an important area of research;
2 The challenge is to get advantage of real-time data, andrecognise important situations in a timely fashion;
3 EP-SPARQL – a new language for Event Processing andStream Reasoning;
4 Future work: complete implementation, more expressiveformalisms, and adaptive optimizations.
D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
IntroductionEP-SPARQL
Experimental ResultsConclusion
ConclusionExampleSemantics
Thank you! Questions...
ETALIS is open source:http://code.google.com/p/etalis
On-line demo:http://etalis.fzi.de
Contact:[email protected]
D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011
IntroductionEP-SPARQL
Experimental ResultsConclusion
ConclusionExampleSemantics
Jagrati Agrawal, Yanlei Diao, Daniel Gyllstrom, and NeilImmerman.Efficient pattern matching over event streams.In Proceedings of the 28th ACM SIGMOD Conference,pages 147–160, 2008.
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Davide Francesco Barbieri, Daniele Braga, Stefano Ceri,and Michael Grossniklaus.An execution environment for C-SPARQL queries.
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ConclusionExampleSemantics
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ConclusionExampleSemantics
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D. Anicic, P. Fodor, S. Rudolph, N. Stojanovic EP-SPARQL – ETALIS – WWW 2011