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

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

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 SemanticsSSN 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)