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Semantic Sensor Networks and Linked Stream Data

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Invited talk at the 8th Summer School on Ontological Engineering and Semantic Web, on Semantic Sensor Networks and Linked Stream/Sensor Data

Invited talk at the 8th Summer School on Ontological Engineering and Semantic Web, on Semantic Sensor Networks and Linked Stream/Sensor Data

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  • The where clasue for both SPARQL extensions is the same
  • Transcript

    • 1. Semantics, Sensor Networks and Linked Stream/Sensor Data
      8th Summer School on Ontological Engineering and Semantic Web (SSSW2011)Cercedilla, July 15th 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
    • 2. Index
      PART I
      From the [Social] [Semantic] Web… to Sensor Networks… … to the Sensor Web / Internet of Things… … to Semantic Sensor Web and Linked Stream/Sensor Data
    • 3. TheSemantic Web of Virtual Things and Data
      Westart living in a well-organisedvirtualworld…
      “Data ismostly in relationaldatabases and can be exportedtoLinked Data” (Juan Sequeda)
      “GoodRelationsmarkupisconqueringtheworld of products and serviceswithstructuredmetadata” (Martin Hepp)
      “Weknowwhere, what and whom” (SteffenStaab)
      “We can searchforit” (Peter Mika)
      “And we can link allthese data sources” (Tom Heath)

      Disclaimer: “Alltutors and invitedspeakers are equallyimportant and orderisnotimportant. Thosewho do notappearshouldnot be worriedaboutthat” (Enrico Motta and Asun Gómez-Pérez)
      However, thereal worldisfar more heterogeneous and lesswell-organisedthanthe data thatwestore in ourcomputers(thisisnottheonlypropertythatit has, youwillsee more…)
      3
    • 4. Web, Semantic Web, Social Web, Social Semantic…
      4
      Source: No idea about copyright (sorry…)
    • 5. Sensor Networks
      Increasing availability of cheap, robust, deployable sensors as ubiquitous information sources
      Dynamic and reactive, but noisy, and unstructured data streams
      Source: Antonis Deligiannakis
    • 6. Parts of a Sensor
      Sensing equipment
      Internal (“built-in”)
      External
      CPU
      Memory
      Battery
      Radio to transmit/receive data from other sensors
      6
      Source: Antonis Deligiannakis
    • 7. Who are theendusers of sensor networks?
      Theclimatechangeexpert, or a simple citizen
      Source: Dave de Roure
    • 8. Notonlyenvironmentalsensors, butmanyothers…
      8
      Weather Sensors
      Sensor Dataset
      GPS Sensors
      Satellite Sensors
      Camera Sensors
      Source: H Patni, C Henson, A Sheth
    • 9. How do wemakethesesensors more accessible?
      9
      Source: SemsorGrid4Env consortium
    • 10. 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
      10
      Source: Adaptedfrom Alan Smeaton’sinvitedtalk at ESWC2009
    • 11. 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” compuyter scientists (and even Semantic Web researchers)
      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
    • 12. A set of challenges in sensor data management
      Provisioning
      Complexity of acquisition: distributed sources, data volumes, uncertainty, data quality, incompleteness
      Pre-processing incoming data: calibration on instruments (specific), lack of re-grid, calibration, gap-filling features
      Tools for data ingestion needed: generic, customizable, provide estimates, uncertainty degree, etc.
      Spatial/temporal
      Analysis, modeling
      Discovery: identify sources, metadata
      Data quality: gaps, faulty data, loss, estimates
      Analysis models
      Republish analytic results, computations, 
      Workflows for data stream processing
      12
      Source: Data Management in theWorldWide Sensor Web. Balazinska et al. IEEE Pervasive Computing, 2007
    • 13. A set of challenges in sensor data management
      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
      13
      Source: Data Management in the WorldWide Sensor Web. Balazinska et al. IEEE Pervasive Computing, 2007
    • 14. 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
    • 15. Vision (aftersomeiterations, and more to come)
      15
      Source: RWI WorkingGrouponIoT: NetworkedKnowledge
    • 16. 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
      SequedaJ, 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
    • 17. LSD (LinkedStream/Sensor Data?)
      Very popular substance in the 60s
      17
    • 18. Let’schecksomeexamples
      Meteorological data in Spain: automaticweatherstations
      http://aemet.linkeddata.es/
      A number of SSSW2011 studentsinvolved in it
      Open reviewingpossibilitiesavailable at theSemantic Web Journal:
      http://www.semantic-web-journal.net/content/transforming-meteorological-data-linked-data
      Live sensors in Slovenia
      One of our SSSW2011 studentsinvolved in it;-)
      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.
      18
    • 19. PART II
      How to create, publish and consume Linked Stream Data
    • 20. HowtodealwithLinkedStream/Sensor Data
      Ingredients
      Anontologymodel
      Goodpractices in URI definition
      Supportingsemantictechnology
      SPARQL extensions
      Tohandle time and tuplewindows
      Tohandlespatio-temporal constraints
      REST APIstoaccessit
      A couple of lessonslearned
    • 21.
      • Several efforts since approx. 2005
      • 22. State of the art on sensor network ontologies in the report below
      • 23. In 2009, a W3C incubator group was started, which has just finished
      • 24. Lots of good people there
      • 25. Final report: http://www.w3.org/2005/Incubator/ssn/XGR-ssn-20110628/
      • 26. Ontology: http://purl.oclc.org/NET/ssnx/ssn
      • 27. A good number of internal and external references to SSN Ontology
      • 28. http://www.w3.org/2005/Incubator/ssn/wiki/Tagged_Bibliography
      • 29. SSN Ontology paper submitted to Journal of Web Semantics
      SSN ontologies. History
    • 30. Deployment
      System
      OperatingRestriction
      Process
      Device
      PlatformSite
      Data
      Skeleton
      ConstraintBlock
      MeasuringCapability
      Overview of the SSN ontology modules
    • 31. 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
    • 32. 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
    • 33. A usageexample
      Upper
      SWEET
      DOLCE UltraLite
      SSG4Env
      infrastructure
      SSN
      Schema
      Service
      External
      OrdnanceSurvey
      FOAF
      Flood domain
      CoastalDefences
      AdditionalRegions
      Role
      25
    • 34. AEMET Ontology Network
      83 classes
      102 objectproperties
      80 datatypeproperties
      19 instances
      SROIQ(D)
    • 35. HowtodealwithLinkedStream/Sensor Data
      Ingredients
      Anontologymodel
      Goodpractices in URI definition
      Supportingsemantictechnology
      SPARQL extensions
      Tohandle time and tuplewindows
      Tohandlespatio-temporal constraints
      REST APIstoaccessit
      A couple of lessonslearned
    • 36. Goodpractices in URI Definition
      Sorry, no clearpracticesyet…
    • 37. 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]
    • 38. HowtodealwithLinkedStream/Sensor Data
      Ingredients
      Anontologymodel
      Goodpractices in URI definition
      Supportingsemantictechnology
      SPARQL extensions
      Tohandle time and tuplewindows
      Tohandlespatio-temporal constraints
      REST APIstoaccessit
      A couple of lessonslearned
    • 39. 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 { …
      31
      Semantically Integrating Streaming and Stored Data
    • 40. SPARQL-STR v1
      32
      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
    • 41. 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
    • 42. SwissEx
      34
      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
    • 43. 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
      35
      Sensors, Mappings and Queries
    • 44. SensorMetadata
      36
      Sensors, Mappings and Queries
      station
      location
      sensors
      model
      properties
    • 45. Sensor Data: Observations
      37
      Sensors, Mappings and Queries
      Heterogeneity
      Integration
    • 46. SPARQL-STR + GSN
    • 47. Uglylittledemo
      Problems
      Toomanysensors
      TooHeterogeneous
      Anysensorsavailable in thisregion?
      Sensorsthatmeasurewind speed?
      How about getting the data?
      39
      Sensors, Mappings and Queries
    • 48. HowtodealwithLinkedStream/Sensor Data
      Ingredients
      Anontologymodel
      Goodpractices in URI definition
      Supportingsemantictechnology
      SPARQL extensions
      Tohandle time and tuplewindows
      Tohandlespatio-temporal constraints
      REST APIstoaccessit
      A couple of lessonslearned
    • 49. Sensor High-level API
      Source: Kevin Page and rest of Southampton’steam at SemsorGrid4Env
    • 50. Sensor High-level API
      Source: Kevin Page and rest of Southampton’steam at SemsorGrid4Env
    • 51. API definition
      Source: Kevin Page and rest of Southampton’steam at SemsorGrid4Env
    • 52. LessonsLearned
      High-level (partI)
      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
    • 53. Semantics, Sensor Networks and Linked Stream/Sensor Data
      8th Summer School on Ontological Engineering and Semantic Web (SSSW2011)Cercedilla, July 15th 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)

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