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Ontology-based Access toSensor Data StreamsJean-Paul CalbimonteSupervisor: Oscar CorchoOntology Engineering GroupFacultad ...
2OutlineMotivationBackgroundConclusionsSemantic stream query processingSensor metadata characterizationOntology-based Acce...
Motivation3from Sensor Networksto the Sensor Weband the Semantic Sensor Web
Sensors4http://www.flickr.com/photos/wouterh/2409251427/data capturedifferent Sensor providerstransmission. . .. . .data s...
Sensor Networks and the Web5Sensor Networksusersapplicationsdata streamsVolumeVelocityVariety WEBUniversal Web-based acces...
Querying the semantic sensor Web6e.g. publish sensor data as RDF/Linked Data?URIs as names of thingsHTTP URIsuseful inform...
Research questions & hypotheses7Ontology models to query real-time sensor data streams?Access heterogeneous SPEs using ont...
Sensor Data: ObservationsCitizen ScienceMultiple publishersHeterogeneityMetadata quality8
Sensor data: observations99
Characterizing semantic sensor metadata10usersapplicationsWEBCharacterizing sensor data, deriving semanticmetadata from th...
Research questions & hypotheses11Data representation suitable for extracting data featuresthat characterize a set of senso...
Contributions12 SPARQL extensions & formalization rewriting to algebra expressions using declarative mappings results ...
Limitations13L1: Rewriting  medium sampling throughput, e.g. Env. monitoringL2: Query expressivity  is limited to underl...
14OutlineMotivationBackgroundConclusionsSemantic stream query processingSensor metadata characterizationOntology-based Acc...
Sensor data streams & events15(temp,hum,pres) τi(36.2,89,4) τimilford1(35.6,87,4) τi-1(37.2,88,4) τi+1watford7. . .(37.6,8...
Querying streams & events16w1 w2windowsSELECT attribute FROM stream [NOW -10 MIN]streaming tuplesQueryprocessorquery resul...
Stream Processing Engines (SPE)17Data Stream Management Systems (DSMS)Complex Event Processors (CEP)Sensor Data Middleware...
Extracting data from relational databases18WEBOntology-baseddata accessone-off SPARQLqueriesdata as RDFrelational database...
Summary19Existing SPEs available and producing data streamsOntology-based access only for stored dataSPARQL query language...
20OutlineMotivationBackgroundConclusionsSemantic stream query processingSensor metadata characterizationOntology-based Acc...
RDF Streams21s,p,o<aemet:observation1, qudt:hasNumericValue, “15.5”><aemet:observation1, ssn:observedBy, aemet:Sensor3>For...
SPARQLStream extensions22SELECT (MAX(?temperature) AS ?maxtemp) ?sensorWHERE {?obs ssn:observedBy ?sensor.?obs ssn:observa...
Streaming SPARQL execution approaches23Extend RDF for streaming dataExtend SPARQL for streaming RDFUse a SPE internally fo...
Mapping SPE schemas and ontologies24wan7timed: datetime PKsp_wind: floattimed sp_wind1 3.42 5.63 11.24 1.25 3.1.. …Queries...
http://swissex.ch/data#Wan7/WindSpeed/ObsValue{timed}sp_windhttp://swissex.ch/data#Wan7/WindSpeed/Observation{timed}http:/...
Query rewritingSELECT ?windspeedFROM STREAM <http://ssg4env.eu/SensorReadings.srdf>[NOW–5 HOUR TO NOW]WHERE {?obs a ssn:Ob...
Ontology-based query rewriting27QueryrewritingQueryProcessingClientSPARQLStream[tuples][triples/bindings]Algebraexpression...
Evaluation of query rewriting overhead28H5: Query rewriting Pull & Push delivery acceptable overheadNative execution w/o...
29OutlineMotivationBackgroundConclusionsSemantic stream query processingSensor metadata characterizationOntology-based Acc...
Characterizing semantic sensor metadata30WEBGSNAir Pressure?Air Temperature?Already classified time seriesUnclassified inp...
Deriving Semantic Metadata31RepresentationClassificationMetadata
0 1 2 3 4 5 6 7 8 9 103.653.73.753.83.853.93.9544.054.10 1 2 3 4 5 6 7 8 9 103.73.753.83.853.93.9544.054.1Piecewise Linear...
Linear Approximations33adac0π/2-π/4π/4abcdKey: segment slopes (angles)Divide the angle space in sectorsdistribution of ang...
Experiments SwissExConfusion matrix SwissExTraining-Test datasetsSwissExperiment AEMET34
Experiments AEMETConfusion matrix AEMETH6: Sensor data series find characteristic patterns make it recognizable among ot...
Evaluation vs SAX36H7: Slope representations type of data: semantic property learned through classification
Semantic Sensor Metadataswissex:Sensor1rdf:type ssn:Sensor;ssn:onPlatform swissex:Station1;ssn:observes cf-property:wind_s...
38OutlineMotivationBackgroundConclusionsSemantic stream query processingSensor metadata characterizationOntology-based Acc...
ConclusionsH1: Sensor streaming data  instances of an ontology modelH2: SPARQL extensions  streaming operators & continu...
Conclusions40H4: Ontology-based streaming queries  abstract expressions concrete executable SPE queriesInstantiate, exec...
Conclusions41H6: Sensor data series  analyze in order to find characteristic patternsmake it recognizable among other typ...
Future directions42WEBSPARQLStream queriesPublishing Linked Stream DataCurrently staticSPARQL streamingstandardsDereferenc...
Future directionsWEBSensor pattern classificationCombine with queryprocessingLive data classificationStatistical & quality...
Ontology-based Access toSensor Data StreamsJean-Paul CalbimonteSupervisor: Oscar CorchoOntology Engineering GroupFacultad ...
45
SSN Ontology with other ontologies46W3C SSN Ontologytool for modeling our sensor datacombine with domain ontologies
Algebra construction47timed,sp_windπωσ sp_wind>105 Hourwan7windsensor1 windsensor2
Static optimization48timed,sp_windπωσ sp_wind>105 Hourwan7timed,windvalueπωσ windvalue>105 Hourwindsensor1timed,windvalueπ...
SPARQL Streaming extensions49
SPARQL Stream features50
SRBench51
RDF Streams and SPARQLStream52RDF StreamTime windowWindow-Stream
Mappings53Subject, predicate, objectGiven a triple pattern t p = (sp, pp,op), the semantics of its evaluation over alation...
Rewrite to algebra54Then, the evaluation of gp can be represented as the following algebra expression:eval (t p,M) = ωts,t...
Rewriting and Execution Process55
Execution process56
SRBench Datasetsreal-world U.S. weather data1first & largest sensor dataset in LOD57LinkedSensorDataLinkedSensorMetadata L...
SRBench Queries58graph pattern matchingsolution modifierquery formSPARQL 1.1reasoningstreamingdata accessand, filter, unio...
Query Features59Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q171.Graph patternmatchingA A,F,O A A,F A A,F,U A A...
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  1. 1. Ontology-based Access toSensor Data StreamsJean-Paul CalbimonteSupervisor: Oscar CorchoOntology Engineering GroupFacultad de Informática, Universidad Politécnica de Madridjp.calbimonte@upm.esPhD Thesis Defense18.4.2013
  2. 2. 2OutlineMotivationBackgroundConclusionsSemantic stream query processingSensor metadata characterizationOntology-based Access to Sensor Data StreamsHypotheses & contributionsChallenges
  3. 3. Motivation3from Sensor Networksto the Sensor Weband the Semantic Sensor Web
  4. 4. Sensors4http://www.flickr.com/photos/wouterh/2409251427/data capturedifferent Sensor providerstransmission. . .. . .data streams
  5. 5. Sensor Networks and the Web5Sensor Networksusersapplicationsdata streamsVolumeVelocityVariety WEBUniversal Web-based access to Sensor data
  6. 6. Querying the semantic sensor Web6e.g. publish sensor data as RDF/Linked Data?URIs as names of thingsHTTP URIsuseful information when URIis dereferencedLink to other URIsusersapplicationsWEBUse ontology models to continuously query real-time data streams originated from sensors?1static vs. streamsone-off vs. continuous
  7. 7. Research questions & hypotheses7Ontology models to query real-time sensor data streams?Access heterogeneous SPEs using ontologies as anoverarching data model?SPARQL streaming extensions for querying data from SPEs(stream processing engines)?1H1: Sensor streaming data  instances of an ontology modelH2: SPARQL extensions  streaming operators & continuous processingH3: Ontology-based streaming queries  rewritten to relational-basedqueries using mappingsH4: Ontology-based streaming queries  abstract expressions concrete executable SPE queriesH5: Query rewriting  Pull & Push delivery  acceptable overhead
  8. 8. Sensor Data: ObservationsCitizen ScienceMultiple publishersHeterogeneityMetadata quality8
  9. 9. Sensor data: observations99
  10. 10. Characterizing semantic sensor metadata10usersapplicationsWEBCharacterizing sensor data, deriving semanticmetadata from the sensor observations2different publishersdifferent metadatapublish streamsSearch/query relevantdata sources?GSN
  11. 11. Research questions & hypotheses11Data representation suitable for extracting data featuresthat characterize a set of sensor streams?Classification and mining techniques to characterizesensor data streams?2H6: Sensor data series  find characteristic patternsmake it recognizable among other typesH7: Slope representations  semantic properties such as the type of data learned with classification techniques acceptable precision
  12. 12. Contributions12 SPARQL extensions & formalization rewriting to algebra expressions using declarative mappings results data translation query evaluation pluggable to ≠ SPEs query rewriting using R2RML mappings data representation as slope distributions characterize types of sensor data classifying sensor time series extract metadata features derive semantic properties & R2RMLSPARQLStreamSensor metadata characterizationQueryingMetadata21
  13. 13. Limitations13L1: Rewriting  medium sampling throughput, e.g. Env. monitoringL2: Query expressivity  is limited to underlying SPEs’.L3: Adapters  implemented for custom sources.L4: Querying  only simple entailmentL5: Arbitrarily noisy sensor series  no accurate characterization.L6: Classification  number of sensor time series in training setL7: Data characterization is not computed in real-time, but offline
  14. 14. 14OutlineMotivationBackgroundConclusionsSemantic stream query processingSensor metadata characterizationOntology-based Access to Sensor Data StreamsHypotheses & contributionsChallengesData Streams Continuous queries WindowSPEs Ontology-based data access
  15. 15. Sensor data streams & events15(temp,hum,pres) τi(36.2,89,4) τimilford1(35.6,87,4) τi-1(37.2,88,4) τi+1watford7. . .(37.6,88,7) τi (36.3,89,2) τi+1. . .. . .stream tuplesevent processing
  16. 16. Querying streams & events16w1 w2windowsSELECT attribute FROM stream [NOW -10 MIN]streaming tuplesQueryprocessorquery resultsdatabaseContinuousqueryprocessorquerypushresultspullrequestSPEcontinuous processingone-off queries
  17. 17. Stream Processing Engines (SPE)17Data Stream Management Systems (DSMS)Complex Event Processors (CEP)Sensor Data MiddlewareCQL/StreamBorealisTelegraphCQStreamMillCayugaGEM CEDRNiagaraCQRapideCosmHourglassSStreamWare GSNIBM InfoSphereSybase CEPMicrosoft StreamInsightOracle CEPEsperStreamBaseDiverse query languagesDifferent query capabilitiesDifferent query models
  18. 18. Extracting data from relational databases18WEBOntology-baseddata accessone-off SPARQLqueriesdata as RDFrelational databaseRDB to RDFmappingsstatic dataD2RMorphODEMapster TriplifyUltraWrap MastroR2RMLW3C SSN Ontology
  19. 19. Summary19Existing SPEs available and producing data streamsOntology-based access only for stored dataSPARQL query language not suitable for streamsSPEs are highly heterogeneous in models and queries
  20. 20. 20OutlineMotivationBackgroundConclusionsSemantic stream query processingSensor metadata characterizationOntology-based Access to Sensor Data StreamsHypotheses & contributionsSPARQLStreamChallengesQuery rewritingRDF StreamMappings using R2RML Execution over SPEs
  21. 21. RDF Streams21s,p,o<aemet:observation1, qudt:hasNumericValue, “15.5”><aemet:observation1, ssn:observedBy, aemet:Sensor3>For streams?( s,p,o ,τ)(<aemet:observation1, qudt:hasNumericValue, “15.5”>,34532)timestamped triples• Gutierrez et al. (2007) Introducing time into RDF. IEEE TKDE• Rodríguez et al. (2009) Semantic management of streaming data. SSN
  22. 22. SPARQLStream extensions22SELECT (MAX(?temperature) AS ?maxtemp) ?sensorWHERE {?obs ssn:observedBy ?sensor.?obs ssn:observationResult ?res.?res aemet:hasAirTemperatureValue ?val.?val qu:numericValue ?temperature.}GROUP BY ?sensorSELECT (MAX(?temp) AS ?maxtemp) ?sensorFROM NAMED STREAM <http://aemet.linkeddata.es/observations.srdf> [NOW-1 HOURS]WHERE {?obs ssn:observedBy ?sensor.?obs ssn:observationResult ?res.?res aemet:hasAirTemperatureValue ?val.?val qu:numericValue ?temp.}GROUP BY ?sensorSPARQLStreamNamed streamsTime windowsOther approaches: Streaming SPARQL (2008), C-SPARQL (2009), CQELS(2011), EP-SPARQL (2011), INSTANS (2012)
  23. 23. Streaming SPARQL execution approaches23Extend RDF for streaming dataExtend SPARQL for streaming RDFUse a SPE internally for evaluationQuery rewriting to SPEsRDF Streaming engine from scratchLogic-programming based query evaluation~SimilaritiesDivergencestreamsDSMSsCEPsMiddlewareSPARQLStream
  24. 24. Mapping SPE schemas and ontologies24wan7timed: datetime PKsp_wind: floattimed sp_wind1 3.42 5.63 11.24 1.25 3.1.. …QueriesSELECT sp_windFROM wan7 [NOW -5 HOUR]WHERE sp_wind >10SPESPE data schemasssn:ObservationOntology modelsSPARQLStream QueriesStream-to-ontologymappingsSELECT ?wspeedFROM STREAM <SensorReadings.srdf> [NOW–5 HOUR]WHERE {?obs a ssn:ObservationValue;qudt:numericalValue ?wspeed;FILTER (?wspeed>10) }
  25. 25. http://swissex.ch/data#Wan7/WindSpeed/ObsValue{timed}sp_windhttp://swissex.ch/data#Wan7/WindSpeed/Observation{timed}http://swissex.ch/data#Wan7/ WindSpeed/ObsOutput{timed}sweetSpeed:WindSpeedCreating Mappings25wan7timed: datetime PKsp_wind: floatssn:ObservationValuequdt:numericValuexsd:decimalssn:SensorOutputssn:Observationssn:hasValuessn:observationResultssn:Propertyssn:observedProperty:Wan4WindSpeed a rr:TriplesMapClass; rr:tableName "wan7";rr:subjectMap [rr:template "http://swissex.ch/data#Wan7/WindSpeed/ObsValue/{timed}";rr:class ssn:ObservationValue; rr:graph ssg:swissexsnow.srdf ];rr:predicateObjectMap [ rr:predicateMap [ rr:predicate qudt:numericValue ];rr:objectMap [ rr:column "sp_wind” rr:datatypexsd:decimal]];.W3C R2RML Mapping Language
  26. 26. Query rewritingSELECT ?windspeedFROM STREAM <http://ssg4env.eu/SensorReadings.srdf>[NOW–5 HOUR TO NOW]WHERE {?obs a ssn:ObservationValue;qudt:numericalValue ?windspeed;FILTER (?windspeed>10) }SELECT sp_wind FROM wan7 [FROM NOW-5 HOURS TO NOW]WHERE sp_wind >10timed,sp_windπωσsp_wind>105 Hourwan7SELECT sp_wind FROM wan7.win:time(5 hour)WHERE sp_wind >10http://montblanc.slf.ch:22001/multidata?vs[0]=wan7&field[0]=wind_speed_scalar_av&c_min[0]=10&from=15/05/2012+05:00:00&to=15/05/2012+10:00:00http://api.cosm.com/v2/feeds/14321/datastreams/4?start=2012-05-15T05:00:00Z&end=2012-05-15T10:00:00ZQueryrewritingR2RMLSNEE (DSMS)Esper (DSMS)GSN (middlwr)Cosm(middlwr)26H4: Ontology-based streaming queries abstract expressions concrete executable SPE queriesH3: Ontology-based streaming queries rewritten to relational-basedqueries using mappingsSPARQLStream
  27. 27. Ontology-based query rewriting27QueryrewritingQueryProcessingClientSPARQLStream[tuples][triples/bindings]AlgebraexpressionR2RMLMappingsSPARQLStream query processingSELECT ?windspeedFROM STREAM <http://ssg4env.eu/SensorReadings.srdf>[NOW–5 HOUR]WHERE {?obs a ssn:ObservationValue;qudt:numericalValue ?windspeed;FILTER (?windspeed>10) }SELECT sp_windFROM wan7.win:time(5 hour)WHERE sp_wind >10π timed,sp_windωσsp_wind>105 Hourwan7DatatranslationSNEEEsperGSNCosmpull/pushhttps://github.com/jpcik/morph-streamsOtherH1: Sensor streaming data instances of an ontology modelH2: SPARQL extensions  streamingoperators & continuous processing
  28. 28. Evaluation of query rewriting overhead28H5: Query rewriting Pull & Push delivery acceptable overheadNative execution w/o rewritingExecution with rewritingPull & Push deliveryEnd-to latencyAdapted Esper benchmark
  29. 29. 29OutlineMotivationBackgroundConclusionsSemantic stream query processingSensor metadata characterizationOntology-based Access to Sensor Data StreamsHypotheses & contributionsRepresentationChallengesClassification Metadata
  30. 30. Characterizing semantic sensor metadata30WEBGSNAir Pressure?Air Temperature?Already classified time seriesUnclassified input seriescompare
  31. 31. Deriving Semantic Metadata31RepresentationClassificationMetadata
  32. 32. 0 1 2 3 4 5 6 7 8 9 103.653.73.753.83.853.93.9544.054.10 1 2 3 4 5 6 7 8 9 103.73.753.83.853.93.9544.054.1Piecewise Linear Approximation32Reflect data trendsApply with different resolutionsApplicable for different ratesOnline computation cheapLinear segmentsTime seriestimeReduce numerosity
  33. 33. Linear Approximations33adac0π/2-π/4π/4abcdKey: segment slopes (angles)Divide the angle space in sectorsdistribution of angles in training setcompute linear approximationcompute slope distributionK-nearest neighbor classification213
  34. 34. Experiments SwissExConfusion matrix SwissExTraining-Test datasetsSwissExperiment AEMET34
  35. 35. Experiments AEMETConfusion matrix AEMETH6: Sensor data series find characteristic patterns make it recognizable among other types35Classification according to typeFPs on subclasses of the same property
  36. 36. Evaluation vs SAX36H7: Slope representations type of data: semantic property learned through classification
  37. 37. Semantic Sensor Metadataswissex:Sensor1rdf:type ssn:Sensor;ssn:onPlatform swissex:Station1;ssn:observes cf-property:wind_speed.swissex:Sensor2rdf:type ssn:Sensor;ssn:onPlatform swissex:Station1;ssn:observes cf-property:air_temperature.37station1W3C SSN OntologyDerive semantic metadata propertiescf-property:wind_speed rdf:type dim:VelocityOrSpeed;rdfs:label "wind speed";ssn:isPropertyOf cf-feature:wind;qu:propertyType qu:scalar;qu:generalQuantityKind qu:speed.Raw sensor data Semantic metadata
  38. 38. 38OutlineMotivationBackgroundConclusionsSemantic stream query processingSensor metadata characterizationOntology-based Access to Sensor Data StreamsHypotheses & contributionsChallenges
  39. 39. ConclusionsH1: Sensor streaming data  instances of an ontology modelH2: SPARQL extensions  streaming operators & continuous processingH3: Ontology-based streaming queries  rewritten to relational-basedqueries using mappingsMapping sensor data to ontology instances, e.g. SSN OntologySPARQLStream  data model, extensions syntax, semanticsSPARQLStream  semantics of query rewriting to relational steamingalgebra usage of declarative mappings (W3C R2RML)Calbimonte, Corcho & Gray. Enabling ontology-based access to streaming data sources. ISWC 2010Gray, García-Castro, Kyzirakos, Karpathiotakis, Calbimonte, Page et al. A semantically enabled servicearchitecture for mashups over streaming and stored data. ESWC 2011Gray, Sadler, Kit, Kyzirakos, Karpathiotakis, Calbimonte, Page, García-Castro, et al. A semantic sensorweb for environmental decision support applications. Sensors, MDPI, 2011Calbimonte, Corcho & Gray. Ontology-based Access to Streaming Data. In Posters ESWC 201039
  40. 40. Conclusions40H4: Ontology-based streaming queries  abstract expressions concrete executable SPE queriesInstantiate, execute  ≠ SPEs: SNEE (DSMS), Esper (CEP), GSN & Cosm (Middlwr) Available implementation application in different domainsH5: Query rewriting  Pull & Push delivery  evaluation overheadSPARQLStream  evaluation overhead wrt. native executionPush & pull delivery evaluationCalbimonte, Jeung, Corcho & Aberer. Enabling Query Technologies for the Semantic Sensor Web. IJSWIS 2012.Calbimonte & Corcho. Evaluating SPARQL Queries over RDF Streams. Linked Data Management: Principlesand Techniques, CRC Press, 2013 (under review)Zhang, Duc, Corcho & Calbimonte. SRBench: A Streaming RDF/SPARQL Benchmark. ISWC 2012.Ruckhaus, Calbimonte, García-Castro & Corcho. Short Paper: From Streaming Data to Linked Data–A CaseStudy with Bike Sharing Systems. ISWC SSN 2012
  41. 41. Conclusions41H6: Sensor data series  analyze in order to find characteristic patternsmake it recognizable among other typesH7: Slope representations  semantic properties such as the type of data learned with classification techniques acceptable precision41Raw observations analysis  slope distribution representation compared with SoA representations i.e. SAXEvaluation of classification task  real world datasets AEMET, SwissEx in presence of noisy data deriving semantic metadataCalbimonte, Yan, Jeung, Corcho & Aberer. Deriving Semantic Sensor Metadata from Raw Measurements.ISWC SSN 2012Calbimonte, Jeung, Corcho, & Aberer. Semantic Sensor Data Search in a Large-Scale Federated SensorNetwork. ISWC SSN 2011
  42. 42. Future directions42WEBSPARQLStream queriesPublishing Linked Stream DataCurrently staticSPARQL streamingstandardsDereferencing streamingdataQuery FederationDistributed sensor dataStatic and streaming sourcesStream Reasoningquery rewriting, expanding queriesExpresivenessIntegrate with the Web of DataInferencing
  43. 43. Future directionsWEBSensor pattern classificationCombine with queryprocessingLive data classificationStatistical & quality analysis Integrate statistic analyisisMappings to statistical modelsData quality filteringParallel Massive Stream Processing Online stream analysisScalable stream processingS4, Storm, StreamcloudHeterogeneity43
  44. 44. Ontology-based Access toSensor Data StreamsJean-Paul CalbimonteSupervisor: Oscar CorchoOntology Engineering GroupFacultad de Informática, Universidad Politécnica de Madrid18.4.2013jp.calbimonte@upm.esPhD Thesis Defense
  45. 45. 45
  46. 46. SSN Ontology with other ontologies46W3C SSN Ontologytool for modeling our sensor datacombine with domain ontologies
  47. 47. Algebra construction47timed,sp_windπωσ sp_wind>105 Hourwan7windsensor1 windsensor2
  48. 48. Static optimization48timed,sp_windπωσ sp_wind>105 Hourwan7timed,windvalueπωσ windvalue>105 Hourwindsensor1timed,windvalueπωσ windvalue>105 Hourwindsensor2
  49. 49. SPARQL Streaming extensions49
  50. 50. SPARQL Stream features50
  51. 51. SRBench51
  52. 52. RDF Streams and SPARQLStream52RDF StreamTime windowWindow-Stream
  53. 53. Mappings53Subject, predicate, objectGiven a triple pattern t p = (sp, pp,op), the semantics of its evaluation over alational streams referenced by a set of mappings M , is given by eval (t p,M), whn algebra expression defined as:eval (t p,M) = ρf s→sp,f p→pp,f o→opπf s,f p,f o(s)where ρ is the relational rename operation and π is the relational projectionon. s is the stream referenced by the mapping µ = f i ndM appi ng(t p,M) and f s,e the functions of µ that generate the projection expressions for producing respece subject, predicate and object, for every tuple of s.For the previous example, the evaluation of t p1 is given by:eval (t p1,M) = ρf s→sp,f p→pp,f o→opπf sµ1(s1.ts),fpµ1(),f oµ1()(s1)The resulting algebra expression projects the s1.ts attribute, applying the f son to create the subject. The functions fpµ1and f oµ1in this case are constants,edicate and object are the same for all tuples of s1. For the evaluation of more coEvaluate query
  54. 54. Rewrite to algebra54Then, the evaluation of gp can be represented as the following algebra expression:eval (t p,M) = ωts,te,δ πf sµ1(s1) ✶ πf sµ2,f oµ2(s1) ✶ πf sµ4,f oµ4(s1) ✶πf sµ5,f oµ5(s1)This expression can be represented as a tree (Figure 4.1), where the leaf nodes are thestreams and the other nodes are the relational streaming operators.Figure 4.1: Tree representation of the evaluation of a SPARQL Stream query rewritten as an alge-bra expression.eval (t p, M ) = ωts,te,δ πf sµ1(s1) ✶ πf sµ2,f oµ2(s1) ✶ πf sµ4,f oµ4(s1) ✶πf sµ5,f oµ5(s1)This expression can be represented as a tree (Figure 4.1), where the leaf nodes are thstreams and the other nodes are the relational streaming operators.Figure 4.1: Tree representation of the evaluation of a SPARQL Stream query rewritten as an algbra expression.
  55. 55. Rewriting and Execution Process55
  56. 56. Execution process56
  57. 57. SRBench Datasetsreal-world U.S. weather data1first & largest sensor dataset in LOD57LinkedSensorDataLinkedSensorMetadata LinkedObservationData~20k US weather stations, ~100k sensorslinks to locations in GeoNames nearbyhurricane & blizzard observations in US~1.73 billion RDF triples~159 million observations1 http://mesowest.utah.eduName Storm Type Date #Triples #Observations Data sizeBill Hurricane Aug. 17 – 22, 2009 231,021,108 21,272,790 ~15 GBIke Hurricane Sep. 01 – 13, 2008 374,094,660 34,430,964 ~34 GBGustav Hurricane Aug. 25 – 31, 2008 258,378,511 23,792,818 ~17 GBBertha Hurricane Jul. 06 – 17, 2008 278,235,734 25,762,568 ~13 GBWilma Hurricane Oct. 17 – 23, 2005 171,854,686 15,797,852 ~10 GBKatrina Hurricane Aug. 23 – 30, 2005 203,386,049 18,832,041 ~12 GBCharley Hurricane Aug. 09 – 15, 2004 101,956,760 9,333,676 ~7 GBBlizzard Apr. 01 – 06, 2003 111,357,227 10,237,791 ~2 GB
  58. 58. SRBench Queries58graph pattern matchingsolution modifierquery formSPARQL 1.1reasoningstreamingdata accessand, filter, union, optionalprojection, distinctselect, construct, askaggregate, subquerysubclass, subproperty, sameAstime window, istreamobservations, sensor metadatageonames, dbpediaselect expr, property pathdstream, rstream17queries
  59. 59. Query Features59Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q171.Graph patternmatchingA A,F,O A A,F A A,F,U A A A A A,F A,F,U A,F A,F,U A,F A,F A,F2. Solution modifier P,D P,D P P P P P,D P P P,D P,D P P P,D P P P3. Query form S S A S C S S S S S S S S S S S S4. SPARQL 1.1 F,P A A,E,M,FA,S N A,E,M A,E,M A,S,M,FA,S,E,M,F,PA,E,M,F,PF,P A,E,M,PP P5. Reasoning C R C A C6. Streaming T T T T T T T,D T T T T T T T T7. Dataset O O O O O O O O,S O,S O,S O,S O,S,G O,S,G O,S,G O,S,D O,S,G,DS1. And, Filter, Union, Optional2. Projection, Distinct3. Select, Construct, Ask4. Aggregate, Subquery, Negation, Expr in SELECT, assignMent,Functions&operators, PropertyPath5. subClassOf, subpRopertyOf, owl:sameAs6. Time-based window, Istream, Dstream,Rstream7. LinkedObservationData, LinkedSensorMetadata, GeoNames, Dbpedia
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