Ingredients for Semantic Sensor Networks
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  • The where clasue for both SPARQL extensions is the same

Ingredients for Semantic Sensor Networks Ingredients for Semantic Sensor Networks Presentation Transcript

  • Ingredients for the Semantic Sensor Web
    Jožef Stefan Institute
    Ljubljana, Slovenia
    September 23rd 2011
    Oscar Corcho
    Facultad de Informática,Universidad Politécnica de Madrid
    Campus de Montegancedosn, 28660 Boadilla del Monte, Madrid
    http://www.oeg-upm.net
    ocorcho@fi.upm.es
    Phone: 34.91.3366605
    Fax: 34.91.3524819
  • Index
    PART I. Motivation
    From Sensor Networks… … to the Sensor Web / Internet of Things… … to Semantic Sensor Web and Linked Stream/Sensor Data
  • Sensor Networks
    Increasingavailability of cheap, robust, deployablesensors as ubiquitousinformationsources
    Source: Antonis Deligiannakis
  • Anexample: SmartCities
    4
    Environmentalsensor nodes
    Parking sensor nodes
    Santander
  • Sensor Networks and Streaming Data
    5
    • Streaming Data
    • Continuously appended data
    • Potentially infinite
    • Time-stamped tuples
    • Continuous queries
    • Latest used in queries
    • Time and tuple-based windows
    (t9, a1, a2, ... , an)
    (t8, a1, a2, ... , an)
    (t7, a1, a2, ... , an)
    ...
    ...
    (t1, a1, a2, ... , an)
    ...
    ...
    Window [t7 - t9]
    Streaming Data
    • Sensor Networks
    • Cheap, Noisy, Unreliable (depends)
    • Low computational, power resources, storage
    • Distributed query execution
    • Routing, Optimization
    Query
    EnablingSemanticIntegration of Streaming Data Sources
  • Who are theendusers of sensor networks?
    Theclimatechangeexpert, or a simple citizen
    Source: Dave de Roure
  • Notonlyenvironmentalsensors, butmanyothers…
    7
    Weather Sensors
    Sensor Dataset
    GPS Sensors
    Satellite Sensors
    Camera Sensors
    Source: H Patni, C Henson, A Sheth
  • How do wemakethesesensors more accessible?
    8
    Source: SemsorGrid4Env consortium
  • The Sensor Web (relatedto Internet of Things)
    Universal, web-based access to sensor data
    Some sensor networkproperties:
    Networked
    Mostlywireless
    Each network with some kind of authority and administration
    Sometimes noisy
    9
    Source: Adaptedfrom Alan Smeaton’sinvitedtalk at ESWC2009
  • Should we care as computer scientists?
    They are mostly useful for environmental scientists, physicists, geographers, seismologists, … [continue for more than 100 disciplines]
    Hence interesting for those computer scientists interested on helping these users… We are many ;-)
    But they are also interesting for “pure” computer scientists
    They address an important set of “grand challenge” Computer Science issues including:
    Heterogeneity
    Scale
    Scalability
    Autonomic behaviour
    Persistence, evolution
    Deployment challenges
    Mobility
    Source: Dave de Roure
  • A semanticperspectiveonthesechallenges
    Sensor data querying and (pre-)processing
    Data heterogeneity
    Data quality
    New inferencecapabilitiesrequiredtodealwith sensor information
    Sensor data modelrepresentation and management
    For data publication, integration and discovery
    Bridgingbetween sensor data and ontologicalrepresentationsfor data integration
    Ontologies: Observations and measurements, time series, etc.
    Eventmodels
    Userinteractionwith sensor data
  • Vision (aftersomeiterations, and more to come)
    12
    RWI WorkingGrouponIoT: NetworkedKnowledgeGluhak et al, 2011. AnArchitecturalBlueprintfor a Real-World Internet', FutureInternet Assembly
  • Semantic Sensor Web / LinkedStream-Sensor Data (LSD)
    A representation of sensor/streamdata followingthestandards of LinkedData
    ButwhatisLinked Data?
  • WhatisLinked Data?
    14
    • An extension of the current Web…
    • … where data are given well-defined and explicitly represented meaning, …
    • … so that it can be shared and used by humans and machines, ...
    • ... better enabling them to work in cooperation
    • And clear principles on how to publish data
  • The fourprinciples (Tim Berners Lee, 2006)
    Use URIs as names for things
    Use HTTP URIs so that people can look up those names.
    When someone looks up a URI, provide useful information, using the standards (RDF*, SPARQL)
    Include links to other URIs, so that they can discover more things.
    http://www.w3.org/DesignIssues/LinkedData.html
    15
    http://www.ted.com/talks/tim_berners_lee_on_the_next_web.html
    http://www.ted.com/talks/tim_berners_lee_on_the_next_web.html
  • Semantic Sensor Web / LinkedStream-Sensor Data (LSD)
    A representation of sensor/streamdata followingthestandards of LinkedData
    Addingsemanticsallowsthesearch and exploration of sensor data withoutany prior knowledge of the data source
    Usingtheprinciples of Linked Data facilitatestheintegration of stream data totheincreasingnumber of Linked Data collections
    Earlyreferences…
    AmitSheth, CoryHenson, and SatyaSahoo, "Semantic Sensor Web," IEEE Internet Computing, July/August 2008, p. 78-83
    Sequeda J, Corcho O. LinkedStream Data: A Position Paper. Proceedingsof the 2nd International WorkshoponSemantic Sensor Networks, SSN 09
    Le-Phuoc D, Parreira JX, Hauswirth M. Challengesin LinkedStream Data Processing: A Position Paper. Proceedingsof the3rd International WorkshoponSemantic Sensor Networks, SSN 10
  • Let’schecksomeexamples
    Meteorological data in Spain: automaticweatherstations
    http://aemet.linkeddata.es/
    Paperunder open review at theSemantic Web Journal
    http://www.semantic-web-journal.net/content/transforming-meteorological-data-linked-data
    Live sensors in Slovenia
    http://sensors.ijs.si/
    ChannelCoastalObservatory in Southern UK
    http://webgis1.geodata.soton.ac.uk/flood.html
    And some more from DERI Galway, Knoesis, CSIRO, etc.
    17
  • AEMET Linked Data
    18
  • JSI Sensors
    19
  • Coastal Channel Observatory and other sources
    20
    Sensors, Mappings and Queries
    Work with Flood environmental sensor data.
    SemSorGrid4Env project www.semsorgrid4env.eu.
  • PART II
    How to create, publish and consume Linked Stream Data
  • HowtodealwithLinkedStream/Sensor Data
    Ingredients
    Anontologymodel
    Goodpractices in URI definition
    Supportingsemantictechnology
    SPARQL extensions
    Tohandle time and tuplewindows
    Tohandlespatio-temporal constraints
    REST APIstoaccessit
    Anotherexample: semanticallyenriching GSN
    A couple of lessonslearned
    • 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
    SSN ontologies. History
  • Deployment
    System
    OperatingRestriction
    Process
    Device
    PlatformSite
    Data
    Skeleton
    ConstraintBlock
    MeasuringCapability
    Overview of the SSN ontology modules
  • deploymentProcesPart only
    Deployment
    System
    OperatingRestriction
    hasSubsystem only, some
    hasSurvivalRange only
    SurvivalRange
    DeploymentRelatedProcess
    hasDeployment only
    System
    OperatingRange
    Deployment
    hasOperatingRange only
    deployedSystem only
    deployedOnPlatform only
    Process
    hasInput only
    inDeployment only
    Device
    Input
    Device
    Process
    onPlatform only
    PlatformSite
    Output
    Platform
    hasOutput only, some
    attachedSystem only
    Data
    Skeleton
    implements some
    isProducedBy some
    Sensor
    Sensing
    hasValue some
    SensorOutput
    sensingMethodUsed only
    detects only
    SensingDevice
    observes only
    SensorInput
    ObservationValue
    isProxyFor only
    Property
    isPropertyOf some
    includesEvent some
    observedProperty only
    observationResult only
    hasProperty only, some
    observedBy only
    Observation
    FeatureOfInterest
    featureOfInterest only
    ConstraintBlock
    MeasuringCapability
    hasMeasurementCapability only
    forProperty only
    inCondition only
    inCondition only
    Condition
    MeasurementCapability
    Overview of the SSN ontologies
  • SSN Ontology. Sensor and environmental properties
    Skeleton
    Property
    Communication
    MeasuringCapability
    hasMeasurementProperty only
    MeasurementCapability
    MeasurementProperty
    Accuracy
    Frequency
    Precision
    Resolution
    Selectivity
    Latency
    DetectionLimit
    Drift
    MeasurementRange
    ResponseTime
    Sensitivity
    EnergyRestriction
    OperatingRestriction
    hasOperatingProperty only
    OperatingProperty
    OperatingRange
    EnvironmentalOperatingProperty
    MaintenanceSchedule
    OperatingPowerRange
    hasSurvivalProperty only
    SurvivalRange
    SurvivalProperty
    EnvironmentalSurvivalProperty
    SystemLifetime
    BatteryLifetime
  • A usageexample
    Upper
    SWEET
    DOLCE UltraLite
    SSG4Env
    infrastructure
    SSN
    Schema
    Service
    External
    OrdnanceSurvey
    FOAF
    Flood domain
    CoastalDefences
    AdditionalRegions
    Role
    27
  • AEMET Ontology Network
    83 classes
    102 objectproperties
    80 datatypeproperties
    19 instances
    SROIQ(D)
  • HowtodealwithLinkedStream/Sensor Data
    Ingredients
    Anontologymodel
    Goodpractices in URI definition
    Supportingsemantictechnology
    SPARQL extensions
    Tohandle time and tuplewindows
    Tohandlespatio-temporal constraints
    REST APIstoaccessit
    Anotherexample: semanticallyenriching GSN
    A couple of lessonslearned
  • Goodpractices in URI Definition
    Sorry, no clearpracticesyet…
  • Goodpractices in URI Definition
    Wehavetoidentify…
    Sensors
    Features of interest
    Properties
    Observations
    Debate betweenbeingobservationor sensor-centric
    Observation-centricseemsto be thewinner
    Forsomedetails of sensor-centric, check [Sequeda and Corcho, 2009]
  • HowtodealwithLinkedStream/Sensor Data
    Ingredients
    Anontologymodel
    Goodpractices in URI definition
    Supportingsemantictechnology
    SPARQL extensions
    Tohandle time and tuplewindows
    Tohandlespatio-temporal constraints
    REST APIstoaccessit
    Anotherexample: semanticallyenriching GSN
    A couple of lessonslearned
  • Queries to Sensor/Stream Data
    SNEEql
    RSTREAM SELECT id, speed, direction
    FROM wind[NOW];
    Streaming SPARQL
    PREFIX fire: <http://www.semsorgrid4env.eu/ontologies/fireDetection#>
    SELECT ?sensor ?speed ?direction
    FROM STREAM <http://…/SensorReadings.rdf> 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: <http://www.semsorgrid4env.eu/ontologies/fireDetection#>
    SELECT ?sensor ?speed ?direction
    FROM STREAM <http://…/SensorReadings.rdf> [RANGE 1 MSEC SLIDE 1 MSEC]
    WHERE { …
    33
    Semantically Integrating Streaming and Stored Data
  • SPARQL-STR v1
    34
    Sensors, Mappings and Queries
    SELECT ?waveheight
    FROM STREAM <www.ssg4env.eu/SensorReadings.srdf>
    [FROM NOW -10 MINUTES TO NOW STEP 1 MINUTE]
    WHERE {
    ?WaveObs a sea:WaveHeightObservation;
    sea:hasValue ?waveheight; }
    SELECT measuredFROM wavesamples [NOW -10 MIN]
    conceptmap-def WaveHeightMeasurement
    virtualStream <http://ssg4env.eu/Readings.srdf>
    uri-as concat('ssg4env:WaveSM_',
    wavesamples.sensorid,wavesamples.ts)
    attributemap-defhasValue
    operation constant
    has-columnwavesamples.measured
    dbrelationmap-def isProducedBy
    toConcept Sensor
    joins-via condition equals
    has-column sensors.sensorid
    has-columnwavesamples.sensorid
    conceptmap-def Sensor
    uri-as concat('ssg4env:Sensor_',sensors.sensorid)
    attributemap-def hasSensorid
    operation constant
    has-column sensors.sensorid
    Query
    translation
    SNEEql
    SPARQLStream
    Query Processing
    Stream-to-Ontology
    mappings
    Client
    Sensor Network
    Data
    translation
    [tuples]
    [triples]
    S2O Mappings
    Source: EnablingOntology-based Access toStreaming Data Sources. Calbimonte JP, Corcho O, Gray AJG. ISWC 2010
  • SPARQL-STR v2
    SPARQLStream algebra(S1 S2 Sm)
    GSN
    Query
    translation
    q
    SNEEql, GSN API
    Sensor Network (S1)
    SPARQLStream (Og)
    Relational DB (S2)
    Query Evaluator
    Stream-to-Ontology
    Mappings (R2RML)
    Client
    Stream Engine (S3)
    RDF Store (Sm)
    Data
    translation
    [tuples]
    [triples]
    Ontology-based Streaming Data Access Service
    Source: PlanetDatadeliverable D1.1 (to be published in Sep 30th 2011) www.planetdata.eu
  • CreatingMappings
    36
    Sensors, Mappings and Queries
    ssn:observedProperty
    ssn:Observation
    ssn:Property
    http://swissex.ch/data#
    Wan7/WindSpeed/Observation{timed}   
    sweetSpeed:WindSpeed
    ssn:observationResult
    wan7
    ssn:SensorOutput
    timed: datetime PK
    sp_wind: float
    http://swissex.ch/data#
    Wan7/ WindSpeed/ ObsOutput{timed}   
    ssn:hasValue
    ssn:ObservationValue
    http://swissex.ch/data#
    Wan7/WindSpeed/ObsValue{timed}
    qudt:numericValue
    xsd:decimal
    sp_wind
  • R2RML
    RDB2RDF W3C Group, R2RML Mappinglanguage:
    http://www.w3.org/2001/sw/rdb2rdf/r2rml/
    37
    Sensors, Mappings and Queries
    :Wan4WindSpeed a rr:TriplesMapClass;
    rr:tableName "wan7";
    rr:subjectMap [ rr:template
    "http://swissex.ch/ns#WindSpeed/Wan7/{timed}";
    rr:classssn:ObservationValue; rr:graphssg:swissexsnow.srdf ];
    rr:predicateObjectMap [ rr:predicateMap [ rr:predicatessn:hasQuantityValue];
    rr:objectMap[ rr:column "sp_wind" ] ]; .
    <http://swissex.ch/ns#/WindSpeed/Wan7/2011-05-20:20:00 >
    a ssn:ObservationValue
    <http://swissex.ch/ns#/WindSpeed/Wan7/2011-05-20:20:00 >
    ssn:hasQuantityValue "4.5"
  • 38
    Red de Ontologías para el Camino de Santiago
    QueryTransformationSemantics
    • ConjunctiveQueries
    • Mapping
    expression
    overstreamingsources
    conjunctive
    query
  • Algebra expressions transformed to GSN API
    39
    Sensors, Mappings and Queries
    π
    SELECT sp_windFROM wan7 [NOW -5 HOUR] WHERE sp_wind >10
    timed,
    sp_wind
    σ
    sp_wind>10
    ω
    5 Hour
    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
    wan7
  • Algebra construction
    40
    Sensors, Mappings and Queries
    π
    timed,
    sp_wind
    windsensor1
    σ
    windsensor2
    sp_wind>10
    ω
    5 Hour
    wan7
  • Staticoptimization
    41
    Sensors, Mappings and Queries
    π
    π
    π
    timed,
    sp_wind
    timed,
    windvalue
    timed,
    windvalue
    σ
    σ
    σ
    sp_wind>10
    windvalue>10
    windvalue>10
    ω
    ω
    ω
    5 Hour
    5 Hour
    5 Hour
    wan7
    windsensor1
    windsensor2
  • HowtodealwithLinkedStream/Sensor Data
    Ingredients
    Anontologymodel
    Goodpractices in URI definition
    Supportingsemantictechnology
    SPARQL extensions
    Tohandle time and tuplewindows
    Tohandlespatio-temporal constraints
    REST APIstoaccessit
    Anotherexample: semanticallyenriching GSN
    A couple of lessonslearned
  • Data Modeling: stRDF
    • stRDF
    • Temporal/spatial data are represented by linear constraints, representing as literals of type strdf:semiLinearPointSet.
    • OGC simple feature geometries (points, polylines, polygons etc.) using the Well-known Text representation
    floodInstances:ModelledFloodIngressDataset
    rdf:typeinfo:Dataset;
    rdfs:label "Modelled flood ingress Dataset" ; time:hasTemporalExtent "[NOW,NOW+12]"^^RegistryOntology:TemporalInterval;
    Services:coversRegionAdditionalRegions:CoastalDefencePartnershipModelledArea; Services:includesFeatureTypeCoastalDefences:FloodPlain ; Services:includesPropertyTypeCoastalDefences:WaterDepth.
    AdditionalRegions:CoastalDefencePartnershipModelledArea
    rdf:typespace:Region; Services:hasSpatialExtent "POLYGON((625145.2823357487 5624227.2548582135, 625145.2823357487 5637255.203057151, 647383.6564885917 5637255.203057151, 647383.6564885917 5624227.2548582135, 625145.2823357487 5624227.2548582135))"^^RegistryOntology:WKT.
    Source: Our NKUA partners at SemsorGrid4Env
    2nd Year Review Meeting - Brussels, 16-17 Nov. 2010
    43
  • Querying: stSPARQL
    Find all WMS services with FOI flood plain that cover the Coastal Defence Partnership modelled area and provide valid information for the next 12 hours
    select distinct ?ENDPOINT
    where { ?SERVICE rdf:typeServices:WebService .
    ?SERVICE Services:hasEndpointReference ?ENDPOINT .
    ?SERVICE Services:hasServiceTypeServices:WMS .
    ?SERVICE Services:hasDataset ?DATASET .
    ?DATASET Services:includesFeatureTypeCoastalDefences:FloodPlain.
    ?DATASET time:hasTemporalExtent ?TIME .
    filter(?TIME contains “[NOW,NOW+12]"^^RegistryOntology:TemporalInterval) .
    ?DATASET Services:coversRegion ?SERVICEREGION .
    ?SERVICEREGION Services:hasSpatialExtent ?SERVICEREGIONGEO .
    AdditionalRegions:CoastalDefencePartnershipModelledArea
    Services:hasSpatialExtent ?COSTALGEO .
    filter(?SERVICEREGIONGEO contains ?COSTALGEO) }
    Source: Our NKUA partners at SemsorGrid4Env
    2nd Year Review Meeting - Brussels, 16-17 Nov. 2010
    44
  • Implementation: STRABON
    SupportforstRDF and SPARQL, plus
    Topologicaloperators in spatialfilters
    DISJOINT, TOUCH, EQUALS, CONTAINS, COVERS, COVERED BY, OVERLAP
    ConstructSpatialGeometries
    e.g. ?geo1 union ?geo2
    Projectionoperation
    e.g. ?geo[1,2]
    Renameoperator
    ConversionFunctionsforexportinggeometries:
    e.g. ToWKT(?geo) AS ?geoAsWKT
    Library thatreturns SPARQL results as a KML document
    45
    Source: Our NKUA partners at SemsorGrid4Env
  • HowtodealwithLinkedStream/Sensor Data
    Ingredients
    Anontologymodel
    Goodpractices in URI definition
    Supportingsemantictechnology
    SPARQL extensions
    Tohandle time and tuplewindows
    Tohandlespatio-temporal constraints
    REST APIstoaccessit
    Anotherexample: semanticallyenriching GSN
    A couple of lessonslearned
  • Sensor High-level API
    Source: Kevin Page and rest of Southampton’steam at SemsorGrid4Env
  • Sensor High-level API
    Source: Kevin Page and rest of Southampton’steam at SemsorGrid4Env
  • API definition
    Source: Kevin Page and rest of Southampton’steam at SemsorGrid4Env
  • HowtodealwithLinkedStream/Sensor Data
    Ingredients
    Anontologymodel
    Goodpractices in URI definition
    Supportingsemantictechnology
    SPARQL extensions
    Tohandle time and tuplewindows
    Tohandlespatio-temporal constraints
    REST APIstoaccessit
    Anotherexample: semanticallyenriching GSN
    A couple of lessonslearned
  • SwissEx
    51
    Sensors, Mappings and Queries
    Global Sensor Networks, deployment for SwissEx.
    Distributedenvironment: GSN Davos, GSN Zurich, etc.
    In each site, a number of sensorsavailable
    Each one withdifferentschema
    Metadatastored in wiki
    Federatedmetadata management:
    Jeung H., Sarni, S., Paparrizos, I., Sathe, S., Aberer, K., Dawes, N., Papaioannus, T., Lehning, M.EffectiveMetadata Management in federatedSensor Networks.  in SUTC, 2010
    Sensor observations
    Sensormetadata
  • Gettingthingsdone
    Transformed wiki metadata to SSN instances in RDF
    Generated R2RML mappings for all sensors
    Implementation of Ontology-basedquerying over GSN
    Fronting GSN with SPARQL-Stream queries
    Numbers:
    28 Deployments
    Aprox. 50 sensors in eachdeployment
    More than 1500 sensors
    Live updates. Lowfrequency
    Access to all metadata/not all data
    52
    Sensors, Mappings and Queries
  • SensorMetadata
    53
    Sensors, Mappings and Queries
    station
    location
    sensors
    model
    properties
  • Sensor Data: Observations
    54
    Sensors, Mappings and Queries
    Heterogeneity
    Integration
  • SPARQL-STR + GSN
  • HowtodealwithLinkedStream/Sensor Data
    Ingredients
    Anontologymodel
    Goodpractices in URI definition
    Supportingsemantictechnology
    SPARQL extensions
    Tohandle time and tuplewindows
    Tohandlespatio-temporal constraints
    REST APIstoaccessit
    Anotherexample: semanticallyenriching GSN
    A couple of lessonslearned
  • LessonsLearned
    High-level (part I)
    Sensor data isyetanothergoodsource of data withsomespecialproperties
    Everythingthatwe do withourrelationaldatasetsorother data sources can be done with sensor data
    Practicallessonslearned (part II)
    Manageseparatelydata and metadata of thesensors
    Data shouldalways be separatedbetweenrealtime-data and historical-data
    Use the time formatxsd:dateTimeand the time zone
    Graphicalrepresentation of data forweeksormonthsisnot trivial anyway
  • Ingredients for the Semantic Sensor Web
    Jožef Stefan Institute
    Ljubljana, Slovenia
    September 23rd 2011
    Oscar Corcho
    Acknowledgments: allthoseidentified in slides + the SemsorGrid4Env team (Jean Paul Calbimonte, Alasdair Gray, Kevin Page, etc.), the AEMET team at OEG-UPM (GhislainAtemezing, Daniel Garijo, José Mora, María Poveda, Daniel Vila, Boris Villazón) + Pablo Rozas (AEMET)