Towards Linked Stream Data Oscar Corcho. Slide 2 of x Contents The concept of Linked Stream Data (LSD) Main challenges addressed so far W3C SSN Ontology.

Towards Linked Stream Data Oscar Corcho

of x Contents The concept of Linked Stream Data (LSD) Main challenges addressed so far W3C SSN Ontology URI definition Supporting technology Some examples Challenges being addressed currently

of x Linked Stream Data A representation of stream data following the principles 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 data collections that form the Linked Open Data cloud Early references… Amit Sheth, Cory Henson, and Satya Sahoo, "Semantic Sensor Web," IEEE Internet Computing, July/August 2008, p. 78-83 Sequeda J, Corcho O. Linked Stream Data: A Position Paper. Proceedings of the 2nd International Workshop on Semantic Sensor Networks, SSN 09 Le-Phuoc D, Parreira JX, Hauswirth M. Challenges in Linked Stream Data Processing: A Position Paper. Proceedings of the 3rd International Workshop on Semantic Sensor Networks, SSN 10

of x Motivation: Sensor Networks Increasing availability of cheap, robust, deployable sensors as ubiquitous information sources Source: Antonis Deligiannakis

of x An example: SmartCities 5 Santander Parking sensor nodes Environmental sensor nodes

of x Sensor Networks and Streaming Data 6 Streaming Data (t9, a1, a2,..., an) (t8, a1, a2,..., an) (t7, a1, a2,..., an)... (t1, a1, a2,..., an)... Streaming Data Window [t7 - t9] Continuously appended data Potentially infinite Time-stamped tuples Continuous queries Latest used in queries Time and tuple-based windows Cheap, Noisy, Unreliable (depends) Low computational, power resources, storage Distributed query execution Routing, Optimization Query Enabling Semantic Integration of Streaming Data Sources Sensor Networks

of x Not only environmental sensors, but many others… 7 Weather Sensors Camera Sensors Satellite Sensors GPS Sensors Sensor Dataset Source: H Patni, C Henson, A Sheth

of x A semantic perspective on the Sensor Web Sensor data querying and (pre-)processing Data heterogeneity Data quality New inference capabilities required to deal with sensor information 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 User interaction with sensor data

of x LSD: Challenges/Topics A model/vocabulary/ontology according to which we can produce RDF data streams URI definition Based on sensors/devices or on observations? How should we encode time in them? Technology to support linked stream data SPARQL extensions to handle time and tuple windows In many cases, also spatio-temporal extensions Tightly coupled with Data Stream Management Systems e.g., MonetDB streaming extension Transformation/Characterisation tools

of x SSN ontology Several efforts since approx. 2005 State of the art on sensor network ontologies in the report below In 2009, a W3C incubator group was started, which has just finished Lots of good people there Final report: http://www.w3.org/2005/Incubator/ssn/XGR- ssn-20110628/ Ontology: http://purl.oclc.org/NET/ssnx/ssn A good number of internal and external references to SSN Ontology http://www.w3.org/2005/Incubator/ssn/wiki/ Tagged_Bibliography SSN Ontology paper submitted to Journal of Web Semantics

of x Skeleton Device Deployment PlatformSite System Process ConstraintBlockMeasuringCapability OperatingRestriction Data Overview of the SSN ontology modules

of x 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 Overview of the SSN ontologies

of x 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 Sensor and environmental properties

of x URI Definition Debate between being observation- centric or sensor-centric Observation-centric seems to be the winner Encoding of time

of x SPARQL-STR 16 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 R2RML Mappings SELECT measured FROM wavesamples [NOW -10 MIN]

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

of x Creating Mappings 18 Sensors, Mappings and Queries wan7 timed: datetime PK sp_wind: float ssn:ObservationVal ue qudt:numericValue xsd:decimal http://swissex.ch/data# Wan7/WindSpeed/ObsValue{timed} sp_wind ssn:SensorOutpu t ssn:Observation ssn:hasValue ssn:observationResult http://swissex.ch/data# Wan7/WindSpeed/Observation{timed} http://swissex.ch/data# Wan7/ WindSpeed/ ObsOutput{timed} ssn:Property ssn:observedProperty sweetSpeed:WindSpeed

of x 19Red de Ontologías para el Camino de Santiago Query Transformation Semantics Conjunctive Queries Mapping conjuncti ve query expression over streaming sources

of x Algebra expressions transformed to GSN API 20 Sensors, Mappings and Queries timed, sp_wind π ω σ sp_wind>10 5 Hour wan7 http://montblanc.slf.ch :22001/ multidata ?vs [0]= wan7 & field [0]= sp_wind & from =15/05/2011+05:00:00& to =15/05/2011+10:00:00& c_vs [0]= wan7 & c_field [0]= sp_wind & c_min [0]=10 SELECT sp_wind FROM wan7 [NOW -5 HOUR] WHERE sp_wind >10

of x Algebra construction 21 Sensors, Mappings and Queries timed, sp_wind π ω σ sp_wind>10 5 Hour wan7 windsensor1 windsensor2

of x Static optimization 22 Sensors, Mappings and Queries timed, sp_wind π ω σ sp_wind>10 5 Hour wan7 timed, windvalue π ω σ windvalue>10 5 Hour windsensor1 timed, windvalue π ω σ windvalue>10 5 Hour windsensor2

of x Technology: Sensor High-level API

of x Let’s check some examples Meteorological data in Spain: automatic weather stations http://aemet.linkeddata.es/ Paper under open review at the Semantic Web Journal http://www.semantic-web- journal.net/content/transforming- meteorological-data-linked-data Live sensors in Slovenia http://sensors.ijs.si/ 26

of x AEMET Linked Data 27

of x JSI Sensors 28

of x SwissEx 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: Jeung H., Sarni, S., Paparrizos, I., Sathe, S., Aberer, K., Dawes, N., Papaioannus, T., Lehning, M.Effective Metadata Management in federated Sensor Networks. in SUTC, 2010 29 Sensor observations Sensor metadata

of x Getting things done 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

of x Sensor Metadata 31 station location model sensors properties

of x Sensor Data: Observations Heterogeneity Integration

of x SPARQL-STR + GSN

of x Pachube2RDF - Architecture Converter To convert pachube data into mySQL records Pachube database Schema that conforms to Pachube result D2R Mappings To map Pachube database with SSN ontology

of x Pachube2RDF - Database

of x Current Situation Core components have been developed Converter Database conforms to Pachube result D2R mappings Pachube result is converted into RDF instances conforming to SSN ontology Difficulties to extract from Pachube result feature-of-interest properties

of x Example of Pachube Result

of x LSD over HTTP Use HTTP as access protocol for streams, as HTTP supports streaming of data Linked Data Streams use RDF as data encoding, and HTTP as access protocol Open HTTP connection, and then serve RDF triples ad infinitum Web Server Client HTTP GET 200 OK STREAM

of x Example Source of stream http://events.play-project.eu/e1 Data (stream of triples) e1:event a :avgTempEvent. e1:event :startTime "2011-01-29"^^xsd:date. e1:event :endTime "2011-01-31"^^xsd:date. loc:Nice :avgTemp [ rdf:value "25" ; :event e1:event ]. … Spec draft at http://km.aifb.kit.edu/sites/lodstream/

of x Pro’s and Con’s Use of standard HTTP servers and clients for streams Simple access using a web browser or other HTTP client Simple publication using a CGI script or Servlet Fits with Linked Data principles, and allows for reuse of tools and best practices (e.g., provenance tracking) Linking via use of URIs in the RDF stream Potential overhead in using HTTP and RDF (lower-level protocols and data formats might be more efficient) Javier D. Fernández, Miguel A. Martínez-Prieto, Claudio Gutierrez, and Axel Polleres. Binary RDF Representation for Publication and Exchange (HDT), W3C Member Submission 30 March 2011.Binary RDF Representation for Publication and Exchange (HDT)

Towards Linked Stream Data Oscar Corcho

Towards Linked Stream Data Oscar Corcho. Slide 2 of x Contents The concept of Linked Stream Data (LSD) Main challenges addressed so far W3C SSN Ontology.

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Towards Linked Stream Data Oscar Corcho. Slide 2 of x Contents The concept of Linked Stream Data (LSD) Main challenges addressed so far W3C SSN Ontology.

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Presentation on theme: "Towards Linked Stream Data Oscar Corcho. Slide 2 of x Contents The concept of Linked Stream Data (LSD) Main challenges addressed so far W3C SSN Ontology."— Presentation transcript:

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