1. Ontology-based Stream/Sensor Data Modeling Presented by: Ashraf Heydari Supervisor: Dr. Kahani

2. Outline Introduction & Motivation Approach ▫Ontology Model ▫URI Definition ▫SPARQL Extensions ▫Example Conclusions References 2

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4. Sensor Networks Increasing availability of cheap, robust, deployable sensors as ubiquitous information sources Dynamic and reactive, but noisy, and unstructured data streams 4

5. Different Kinds of Sensors 5 Camera Sensors Satellite Sensors GPS Sensors Sensor Dataset Weather Sensors

6. The Sensor Web 6 Universal, web-based access to sensor data

7. Streaming Data 7 Continuously appended data Potentially infinite Time-stamped tuples Continuous queries Changes of values over time Latest used in queries (t9, a1, a2,..., an) (t8, a1, a2,..., an) (t7, a1, a2,..., an)... (t1, a1, a2,..., an)... Streaming Data

8. A Set of Challenges in Sensor Data Management 8 Provisioning ▫Complexity of acquisition: distributed sources, data volumes ▫Pre-processing incoming data ▫Tools for data ingestion needed Spatial/temporal Analysis, modeling ▫Discovery: identify sources, metadata ▫Data quality: faulty data, loss, estimates ▫Analysis models ▫Republish analytic results ▫Workflows for data stream processing

9. A Set of Challenges in Sensor Data Management 9 Interoperability ▫Data aggregation/integration Uncertainty, data quality ▫Noise, failures, measurement errors, confidence, trust Distributed processing ▫High volume, time critical ▫Fault-tolerance ▫Load management ▫Stream processing features ▫Continuous queries ▫Live & historical data

10. A Set of Challenges in Sensor Data Management 10 Interoperability ▫Data aggregation/integration Uncertainty, data quality ▫Noise, failures, measurement errors, confidence, trust Distributed processing ▫High volume, time critical ▫Fault-tolerance ▫Load management ▫Stream processing features ▫Continuous queries ▫Live & historical data

11. A Semantic Perspective on These Challenges 11 Sensor data model representation and management ▫For data publication, integration and discovery ▫Bridging between sensor data and ontological representations for data integration ▫Ontologies: Observations and measurements, time series, etc. ▫Event models Sensor data querying and (pre-)processing ▫Data heterogeneity ▫Data quality ▫New inference capabilities required to deal with sensor information User interaction with sensor data

12. Semantic Sensor Web/ Linked Stream-Sensor Data (LSD) 12 A representation of sensor/stream data following the standards of Linked Data ▫Adding semantics allows the search and exploration of sensor data without any prior knowledge of the data source ▫Using the principles of Linked Data facilitates the integration of stream data to the increasing number of Linked Data collections

13. Semantic Sensor Web/ Linked Stream-Sensor Data (LSD) 13

14. Some Examples 14 Meteorological data in Spain: automatic weather stations ▫http://aemet.linkeddata.es/http://aemet.linkeddata.es/ Live sensors in Slovenia ▫http://sensors.ijs.si/http://sensors.ijs.si/ Channel Coastal Observatory in Southern UK ▫http://webgis1.geodata.soto n.ac.uk/flood.htmlhttp://webgis1.geodata.soto n.ac.uk/flood.html

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16. How to Deal with Linked Stream/Sensor Data 16 An ontology model URI definition SPARQL extensions ▫To handle time and tuple windows

17. SSN Ontologies. History 17 Several efforts since approx. 2005 In 2009, a W3C incubator group was started, which has just finished Ontology: http://purl.oclc.org/NET/ssnx/ssn A good number of internal and external references to SSN Ontology SSN Ontology paper submitted to Journal of Web Semantics

18. Overview of The SSN Ontology Modules 18 Skeleton Device Deployment PlatformSite System Process ConstraintBlockMeasuringCapability OperatingRestriction Data

19. Overview of The SSN Ontologies 19 Skeleton Device Deployment PlatformSite System onPlatform only hasSubsystem only, some SurvivalRange hasSurvivalRange only OperatingRange hasOperatingRange only hasDeployment only DeploymentRelatedProcess Deployment deploymentProcesPart only deployedSystem only Platform deployedOnPlatform only attachedSystem only Device Sensor SensingDevice Sensing implements some observes only hasMeasurementCapability only inDeployment only SensorInput detects only isProxyFor only ObservationValue SensorOutput hasValue some isProducedBy some Process hasInput only hasOutput only, some Input Output Observation observedBy only featureOfInterest only observationResult only Property observedProperty only hasProperty only, some isPropertyOf some sensingMethodUsed only includesEvent some FeatureOfInterest ConstraintBlock Condition inCondition only MeasuringCapability MeasurementCapability forProperty only OperatingRestriction inCondition only Data

20. SSN Ontology. Sensor and Environmental Properties 20 CommunicationMeasuringCapability MeasurementCapabilityMeasurementProperty hasMeasurementProperty only Accuracy DetectionLimitDrift Frequency MeasurementRange PrecisionResolution ResponseTime Selectivity Sensitivity Latency Skeleton EnergyRestrictionOperatingRestriction OperatingRange OperatingProperty hasOperatingProperty only EnvironmentalOperatingPropertyMaintenanceSchedule SurvivalRangeSurvivalProperty hasSurvivalProperty only EnvironmentalSurvivalPropertySystemLifetimeBatteryLifetime OperatingPowerRange Property

21. A Usage Example 21 SWEET Service Coastal Defences Ordnance Survey Additional Regions Role DOLCE UltraLite Schema FOAF Upper External SSG4Env infrastructure Flood domain SSN

22. How to Deal with Linked Stream/Sensor Data 22 An ontology model URI definition SPARQL extensions ▫To handle time and tuple windows

23. URI Definition 23 No clear practices yet We have to identify… ▫Sensors ▫Features of interest ▫Properties ▫Observations Debate between being observation or sensor-centric ▫Observation-centric seems to be the winner

24. How to Deal with Linked Stream/Sensor Data 24 An ontology model URI definition SPARQL extensions ▫To handle time and tuple windows

25. SPARQL Stream 25 Example: “provide me with the wind speed observations over the last minute in the Solent Region ”... (, t i-1 ), (, t i ), (, t i+1 ),... cd:Observation xsd:double cd:observationResult... (, t i ), (, t i+1 ),... STREAM RDF-Stream

26. SPARQL Stream 26 Example: “provide me with the wind speed observations over the last minute in the Solent Region ” cd:Observation xsd:double cd:observationResult PREFIX cd: PREFIX sb: PREFIX rdf: SELECT ?windspeed ?windts FROM STREAM [ NOW – 1 MINUTE TO NOW – 0 MINUTES ] WHERE { ?WindObs a cd:Observation; cd:observationResult ?windspeed; cd:observationResultTime ?windts; cd:observedProperty ?windProperty; cd:featureOfInterest ?windFeature. ?windFeature a cd:Feature; cd:locatedInRegion cd:SolentCCO. ?windProperty a cd:WindSpeed. } cd:Feature cd:featureOfInterest cd:Property cd:observedProperty cd:locatedInRegion cd:Region

27. Queries to Sensor/Stream Data 27 SNEEql RSTREAM SELECT id, speed, direction FROM wind[NOW]; Streaming SPARQL PREFIX fire: SELECT ?sensor ?speed ?direction FROM STREAM WINDOW RANGE 1 MS SLIDE 1 MS WHERE { ?sensor a fire:WindSensor; fire:hasMeasurements ?WindSpeed, ?WindDirection. ?WindSpeed a fire:WindSpeedMeasurement; fire:hasSpeedValue ?speed; fire:hasTimestampValue ?wsTime. ?WindDirection a fire:WindDirectionMeasurement; fire:hasDirectionValue ?direction; fire:hasTimestampValue ?dirTime. FILTER (?wsTime == ?dirTime) } C-SPARQL REGISTER QUERY WindSpeedAndDirection AS PREFIX fire: SELECT ?sensor ?speed ?direction FROM STREAM [RANGE 1 MSEC SLIDE 1 MSEC] WHERE { …

28. SPARQL-STR v1 SELECT ?waveheight FROM STREAM [FROM NOW -10 MINUTES TO NOW STEP 1 MINUTE] WHERE { ?WaveObs a sea:WaveHeightObservation; sea:hasValue ?waveheight; } Query translation Query Processing Client Stream-to-Ontology mappings SPARQLStream [ tuples ] Sensor Network Data translation [ triples ] SNEEql conceptmap-def WaveHeightMeasurement virtualStream uri-as concat('ssg4env:WaveSM_', wavesamples.sensorid,wavesamples.ts) attributemap-def hasValue operation constant has-column wavesamples.measured dbrelationmap-def isProducedBy toConcept Sensor joins-via condition equals has-column sensors.sensorid has-column wavesamples.sensorid conceptmap-def Sensor uri-as concat('ssg4env:Sensor_',sensors.sensorid) attributemap-def hasSensorid operation constant has-column sensors.sensorid S2O Mappings SELECT measured FROM wavesamples [NOW -10 MIN] 28

29. SPARQL-STR v2 29 Query translation Query Evaluator Client Stream-to-Ontology Mappings (R2RML) SPARQL Stream [tuples] Stream Engine (S 3 ) Ontology-based Streaming Data Access Service Relational DB (S 2 ) Sensor Network (S 1 ) RDF Store (S m ) Data translation [triples] SNEEql, GSN API GSN

30. SwissEx 30 Global Sensor Networks, deployment for SwissEx. Distributed environment: GSN Davos, GSN Zurich, etc. ▫In each site, a number of sensors available ▫Each one with different schema Metadata stored in wiki ▫Federated metadata management

31. Getting things done 31 Transformed wiki metadata to SSN instances in RDF Generated R2RML mappings for all sensors Implementation of Ontology-based querying over GSN Fronting GSN with SPARQL-Stream queries Numbers: ▫28 Deployments ▫Aprox. 50 sensors in each deployment ▫More than 1500 sensors ▫Live updates. Low frequency ▫Access to all metadata/not all data

32. Sensor Metadata 32 station location model sensors properties

33. Sensor Data: Observations 33 GSN (Global Sensor Networks) is a database software middleware designed to facilitate the deployment and programming of sensor networks. The software takes data (either directly from a sensor or from a CSV file), enters it into a database and provides a web-based query interface. It is completely generalised and able to handle sensors of all types.

34. SPARQL-STR + GSN 34

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36. Conclusions 36 Sensor data is yet another good source of data with some special properties Everything that we do with our relational datasets or other data sources can be done with sensor data Adding semantics allows the search and exploration of sensor data without any prior knowledge of the data source Using the principles of Linked Data facilitates the integration of stream data to the increasing number of Linked Data collections

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38. References 38 Semantic Sensor Network XG Final Report, W3C Incubator Group Report 28 June 2011, http://www.w3.org/2005/Incubator/ssn/XGR-ssn-20110628/ http://www.w3.org/2005/Incubator/ssn/XGR-ssn-20110628/ K. Janowicz and M. Compton The Stimulus-Sensor-Observation Ontology Design Pattern and its Integration into the Semantic Sensor Network Ontology. In The 3rd International workshop on Semantic Sensor Networks 2010 (SSN10) in conjunction with the 9th International Semantic Web Conference (ISWC 2010), 2010.The Stimulus-Sensor-Observation Ontology Design Pattern and its Integration into the Semantic Sensor Network Ontology P. Barnaghi, S. Meissner and M. Presser Sense and sensability: Semantic data modelling for sensor networks. In Proceedings of the ICT Mobile Summit 2009, pp. 1-9, 2009.Sense and sensability: Semantic data modelling for sensor networks M. Compton, C. Henson, H. Neuhaus, L. Lefort and A. Sheth A Survey of the Semantic Specification of Sensors. In Proceedings of the 2nd International Workshop on Semantic Sensor Networks (SSN09) at ISWC 2009, pp. 17-32, 2009.A Survey of the Semantic Specification of Sensors M. Compton, H. Neuhaus, K. Taylor and K. Tran Reasoning about Sensors and Compositions. In Proceedings of the 2nd International Workshop on Semantic Sensor Networks (SSN09) at ISWC 2009, pp. 33-48, 2009.Reasoning about Sensors and Compositions P. Barnaghi and M. Presser Publishing Linked Sensor Data. In The 3rd International workshop on Semantic Sensor Networks 2010 (SSN10) in conjunction with the 9th International Semantic Web Conference (ISWC 2010), 2010.Publishing Linked Sensor Data A. Gray, J. Sadler, O. Kit, K. Kyzirakos, M. Karpathiotakis, J. Calbimonte, K. Page, R. Garc´ıa-Castro, A. Frazer, I. Galpin, A. Fernandes, N. Paton, M. Koubarakis, D. De Roure, K. Martinez, A. G´omez-P´erez. A Semantic Sensor Web for Environmental Decision Support Applications. In Sensors 11, no. 9, 2011.A Semantic Sensor Web for Environmental Decision Support Applications R. García Castro, C. Hill and O. Corcho Sensor network ontology suite v2. Deliverable D4.3v2, SemSorGrid4Env SemSorGrid4Env: Semantic Sensor Grids for Rapid Application Development for Environmental Management, 2011.Sensor network ontology suite v2. Deliverable D4.3v2, SemSorGrid4Env H. Neuhaus, M. Compton The Semantic Sensor Network Ontology: A Generic Language to Describe Sensor Assets. In AGILE Workshop Challenges in Geospatial Data Harmonisation, 2009.The Semantic Sensor Network Ontology: A Generic Language to Describe Sensor Assets D.F.Barbieri, D.Braga, S.Ceri, E.Della Valle, M.Grossniklaus Querying RDF Streams with C-SPARQL. In SIGMOD Record, 2010.Querying RDF Streams with C-SPARQL D.F.Barbieri, D.Braga, S.Ceri, E.Della Valle, M.Grossniklaus C-SPARQL: SPARQL for continuous querying. In: WWW '09, 2009.C-SPARQL: SPARQL for continuous querying A. Salehi, M. Riahi, S. Michel, and K. Aberer. GSN, Middleware for Streaming World (Best Demo Award). NCCR-MICS, NCCR-MICS/CL4, 2009. GSN, Middleware for Streaming World

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