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Semantic (Social) Sensor Networks
 

Semantic (Social) Sensor Networks

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Presentation done at the 9th Summer School on Ontological Engineering and the Semantic Web (SSSW2012, http://sssw.org/) in July 2012. Please do treat references to people (e.g., Manfred Hauswirth) and ...

Presentation done at the 9th Summer School on Ontological Engineering and the Semantic Web (SSSW2012, http://sssw.org/) in July 2012. Please do treat references to people (e.g., Manfred Hauswirth) and nationalities (e.g., about Swiss) in the context in which they were done.

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  • - A core ontological model that can be used to describe sensor data streams, including the metadata about the sensor data sources and their observations. We take into account here the ontology developed for this purpose in the context of the W3C Semantic Sensor Network Incubator Group, which can be considered the current standard to be followed. - A set of additional domain ontologies in the area in which sensors are applied and for which they generate measurements (e.g., if we deal with environmental sensors related to water, then ontologies about different aspects of water will be needed). These ontologies must be aligned with the previous core ontology.Guidelines for the generation of identifiers (in the form of URIs, since this is one of the key ingredients of Linked Data) for sensors, their observations and the features of interest that they observe.Supporting Sensor Web programming interfaces (APIs) that make use of the HTTP protocol for serving the corresponding data whenever the previous URIs are dereferenced. Query processing engines that support extended versions of SPARQL (the query language used for Linked Data) and handle some of the most characteristic aspects of data streams, such as time and/or tuple windows. The management of spatio-temporal extensions of this query language may be also useful in this context.
  • - A core ontological model that can be used to describe sensor data streams, including the metadata about the sensor data sources and their observations. We take into account here the ontology developed for this purpose in the context of the W3C Semantic Sensor Network Incubator Group, which can be considered the current standard to be followed. - A set of additional domain ontologies in the area in which sensors are applied and for which they generate measurements (e.g., if we deal with environmental sensors related to water, then ontologies about different aspects of water will be needed). These ontologies must be aligned with the previous core ontology.Guidelines for the generation of identifiers (in the form of URIs, since this is one of the key ingredients of Linked Data) for sensors, their observations and the features of interest that they observe.Supporting Sensor Web programming interfaces (APIs) that make use of the HTTP protocol for serving the corresponding data whenever the previous URIs are dereferenced. Query processing engines that support extended versions of SPARQL (the query language used for Linked Data) and handle some of the most characteristic aspects of data streams, such as time and/or tuple windows. The management of spatio-temporal extensions of this query language may be also useful in this context.
  • - A core ontological model that can be used to describe sensor data streams, including the metadata about the sensor data sources and their observations. We take into account here the ontology developed for this purpose in the context of the W3C Semantic Sensor Network Incubator Group, which can be considered the current standard to be followed. - A set of additional domain ontologies in the area in which sensors are applied and for which they generate measurements (e.g., if we deal with environmental sensors related to water, then ontologies about different aspects of water will be needed). These ontologies must be aligned with the previous core ontology.Guidelines for the generation of identifiers (in the form of URIs, since this is one of the key ingredients of Linked Data) for sensors, their observations and the features of interest that they observe.Supporting Sensor Web programming interfaces (APIs) that make use of the HTTP protocol for serving the corresponding data whenever the previous URIs are dereferenced. Query processing engines that support extended versions of SPARQL (the query language used for Linked Data) and handle some of the most characteristic aspects of data streams, such as time and/or tuple windows. The management of spatio-temporal extensions of this query language may be also useful in this context.
  • - A core ontological model that can be used to describe sensor data streams, including the metadata about the sensor data sources and their observations. We take into account here the ontology developed for this purpose in the context of the W3C Semantic Sensor Network Incubator Group, which can be considered the current standard to be followed. - A set of additional domain ontologies in the area in which sensors are applied and for which they generate measurements (e.g., if we deal with environmental sensors related to water, then ontologies about different aspects of water will be needed). These ontologies must be aligned with the previous core ontology.Guidelines for the generation of identifiers (in the form of URIs, since this is one of the key ingredients of Linked Data) for sensors, their observations and the features of interest that they observe.Supporting Sensor Web programming interfaces (APIs) that make use of the HTTP protocol for serving the corresponding data whenever the previous URIs are dereferenced. Query processing engines that support extended versions of SPARQL (the query language used for Linked Data) and handle some of the most characteristic aspects of data streams, such as time and/or tuple windows. The management of spatio-temporal extensions of this query language may be also useful in this context.
  • - A core ontological model that can be used to describe sensor data streams, including the metadata about the sensor data sources and their observations. We take into account here the ontology developed for this purpose in the context of the W3C Semantic Sensor Network Incubator Group, which can be considered the current standard to be followed. - A set of additional domain ontologies in the area in which sensors are applied and for which they generate measurements (e.g., if we deal with environmental sensors related to water, then ontologies about different aspects of water will be needed). These ontologies must be aligned with the previous core ontology.Guidelines for the generation of identifiers (in the form of URIs, since this is one of the key ingredients of Linked Data) for sensors, their observations and the features of interest that they observe.Supporting Sensor Web programming interfaces (APIs) that make use of the HTTP protocol for serving the corresponding data whenever the previous URIs are dereferenced. Query processing engines that support extended versions of SPARQL (the query language used for Linked Data) and handle some of the most characteristic aspects of data streams, such as time and/or tuple windows. The management of spatio-temporal extensions of this query language may be also useful in this context.
  • The where clasue for both SPARQL extensions is the same
  • Addingsemanticsallowsthesearch and exploration of sensor data withoutany prior knowledge of the data sourceUsingtheprinciples of Linked Data facilitatestheintegration of stream data totheincreasingnumber of Linked Data collections

Semantic (Social) Sensor Networks Semantic (Social) Sensor Networks Presentation Transcript

  • SSSW2012 – The Ninth Summer School on Ontological Engineering and Semantic WebSemantic Sensor Networks(and Social) Semantic Web Oscar Corcho (with the help of Jean Paul Calbimonte) Ontology Engineering Group Facultad de Informática, Universidad Politécnica de Madrid ocorcho@fi.upm.es
  • This is what my talk is going to be about… Application Middleware CQELS REST SPARQL Linked Streams Linked Data COAP VirtualSensors Sensors
  • Was this a Déjà Vu?• Key differences • I am younger (as demonstrated on Tuesday) • We are both vice-directors • But I am vice-director of a most important organisation (SSSW vs DERI)• Lessons learned: • Never let an invited speaker speak before you • Specially if he is the one giving a great tutorial ;-)3
  • Was this a Déjà Vu?• Hence the title of my talk could perfectly be… 4
  • Some work that can be inserted in the picture Application Middleware SPARQL-STR CQELS REST SPARQL Linked Streams Linked DataData Stream COAP VirtualManagement Sensors Systems Sensors
  • Ingredients for the Semantic Sensor WebCore ontological modelAdditional domain ontologiesGuidelines for generation of identifiersSensor Web programming interfaces (e.g.,lsm.deri.ie)Query processing engines http://www.flickr.com/photos/santos/2252824606/
  • AEMET Linked Data (http://aemet.linkeddata.es) 7
  • Semantic Weather (http://www.semanticweather.com) 8
  • Some Slovenian city sensors (http://sensors.ijs.si/) 9
  • Coastal Channel Observatory and other sources (http://webgis1.geodata.soton.ac.uk/flood.html) Flood risk alert:South East England Emergency I have to make planner sense out of all this data wave data Environmental forecasts defenses 10
  • Should we care? Smart citiesEnvironmental sensors Parking sensors 11 SmartSantander Project
  • Some work that can be inserted in the picture Application Middleware SPARQL-STR CQELS REST SPARQL Linked Streams Linked DataData Stream COAP VirtualManagement Sensors Systems Sensors
  • Ingredients for the Semantic Sensor WebCore ontological modelAdditional domain ontologiesGuidelines for generation of identifiersSensor Web programming interfaces (e.g.,lsm.deri.ie)Query processing engines http://www.flickr.com/photos/santos/2252824606/
  • Sensor Network Ontologies Approximately since 2005: Several proposals  State of the art on sensor network ontologies in the report below  Most of them were too project-specific  Not too much reuse  No alignment between them  No ontology design best practices 2009-2011: W3C SSN-XG incubator group  Final report: http://www.w3.org/2005/Incubator/ssn/XGR-ssn-20110628/  SSN 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 soon at the Journal of Web Semantics 2011-__ : W3C SSN Community Group
  • Overview of the SSN ontology
  • Overview of the SSN ontologyDeployment deploymentProcesPart only System OperatingRestriction hasSubsystem only, some hasSurvivalRange only SurvivalRange DeploymentRelatedProcess hasDeployment only System OperatingRange Deployment deployedSystem only hasOperatingRange only deployedOnPlatform only Process inDeployment only Device hasInput only InputPlatformSite onPlatform only Device Process Platform Output attachedSystem only hasOutput only, someData Skeleton isProducedBy some implements some Sensor Sensing hasValue some sensingMethodUsed only SensorOutput detects only SensingDevice observes only ObservationValue SensorInput isProxyFor only Property includesEvent some isPropertyOf some observedProperty only observationResult only observedBy only hasProperty only, some Observation FeatureOfInterest featureOfInterest only MeasuringCapability ConstraintBlock hasMeasurementCapability only forProperty only inCondition only inCondition only MeasurementCapability Condition
  • SSN Ontology. Sensor and environmental propertiesSkeleton Property MeasuringCapability Communication hasMeasurementProperty only MeasurementCapability MeasurementProperty Accuracy Resolution Selectivity Frequency Precision Latency DetectionLimit Drift ResponseTime Sensitivity MeasurementRange OperatingRestriction EnergyRestriction hasOperatingProperty only OperatingRange OperatingProperty EnvironmentalOperatingProperty MaintenanceSchedule OperatingPowerRange hasSurvivalProperty only SurvivalRange SurvivalProperty EnvironmentalSurvivalProperty SystemLifetime BatteryLifetime
  • SSN Ontology with other Ontologies 19
  • A usage example Upper DOLCE SWEET UltraLite SSG4Env infrastructure SSN Schema Service External FOAF Ordnance Survey Flood domain Role Coastal Additional Defences RegionsGarcía-Castro R, Corcho O, Hill C. A Core Ontological Model for Semantic Sensor WebInfrastructures. International Journal on Semantic Web and Information Systems SpecialIssue on Sensor Networks, Internet of Things and Machine-to-Machine Communications,Volume 8, Issue 1, 2013 20
  • AEMET Ontology Network (http://aemet.linkeddata.es/)• 83 classes, 102 object properties, 80 datatype properties• SROIQ(D)
  • Let’s now talk about metadata and social stuff Application Middleware SPARQL-STR CQELS REST SPARQL Linked Streams Linked DataData Stream COAP VirtualManagement Sensors Systems Sensors
  • Ingredients for the Semantic Sensor WebCore ontological modelAdditional domain ontologiesGuidelines for generation of identifiersSensor Web programming interfaces (e.g.,lsm.deri.ie)Query processing engines http://www.flickr.com/photos/santos/2252824606/
  • (Social) Sensor MetadataDo we have enough reliable metadata aboutsensors and observations? NO!!!! People are messy!!! Even if they are scientists who do not use Twitter Let’s start with Swiss people (you know, they are always precise ;-)) 24
  • SwissEx• Global Sensor Networks, deployment for SwissEx. • 28 Deployments, Aprox. 50 sensors in each deployment • More than 1500 sensors • Live updates. Low frequency • Access to all metadata/not all data• Distributed environment: GSN Davos, GSN Zurich, etc. • In each site, a number of sensors available • Each one with different schema• Metadata stored in wiki Sensor observations • 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 Sensor metadata 25
  • Sensor Metadata station location sensors model propertiesSensors, Mappings and Queries 26
  • Sensor Data: Observations Heterogeneity ? IntegrationSensors, Mappings and Queries 27
  • Putting some meaning into place
  • Sensor MetadataDo we have enough reliable metadata aboutsensors and observations? NO!!!! People are messy!!! Even if they are scientists who do not use Twitter Let’s start with Swiss people (you know, they are always precise ;-))Swiss are boring… Let’s now move to the rest of the world (citizen sensing) 29
  • Citizen Sensing: Pachube/COSM ? 30
  • Looking at the Data Air Pressure Temperature31
  • Classifying Sensor Data• Idea: given a new time series, find similar ones in a set of classified time-series• Querying time series • e.g. find a sub-sequence in a time series database• Measuring time series similarity • e.g. are these time series the same?• Time series classification • e.g. classify heart beat series: normal, murmur, etc 32
  • Challenges: Data Summaries • Challenges • Represent the data approximating with fewer linear segments: • Tradeoff between Accuracy vs Numerosity Data buckets33
  • Data summaries: Linear Approximations• We care about the angles π/2 a π/4 a c d b a 0 c -π/4 d 34
  • Use the representation for Classifying• Linear approximation• Compute distribution of the slopes• K-nearest neighbour classification• Training-Test datasets: • SwissExperiment • AEMET 35
  • Experiments: SwissEx and AEMET 36
  • ConclusionsIdentifying Sensor Observations Take a look at the data Machine Learning Techniques Time Series Summarization Clustering Normalization Distance MetricsWork in progress Use more of the social tagging information! Test in Pachube
  • Query processing Application Middleware SPARQL-STR CQELS REST SPARQL Linked Streams Linked DataData Stream COAP VirtualManagement Sensors Systems Sensors
  • Ingredients for the Semantic Sensor WebCore ontological modelAdditional domain ontologiesGuidelines for generation of identifiersSensor Web programming interfaces (e.g.,lsm.deri.ie)Query processing engines http://www.flickr.com/photos/santos/2252824606/
  • SPARQL with streaming extensionsStreaming SPARQLPREFIX fire: <http://www.semsorgrid4env.eu/ontologies/fireDetection#>SELECT ?WindSpeedFROM STREAM <http://…/SensorReadings.rdf> WINDOW RANGE 1 MS SLIDE 1 MSWHERE { ?sensor fire:hasMeasurements ?WindSpeed FILTER (?WindSpeed<30)}C-SPARQLREGISTER QUERY WindSpeedAndDirection ASPREFIX fire: <http://www.semsorgrid4env.eu/ontologies/fireDetection#>SELECT ?sensor ?speed ?directionFROM STREAM <http://…/SensorReadings.rdf> [RANGE 1 MSEC SLIDE 1 MSEC]WHERE { …CQELS… 40
  • SPARQL-StreamSELECT ?windspeed ?tidespeedFROM NAMED STREAM <http://swiss-experiment.ch/data#WannengratSensors.srdf>[NOW-10 MINUTES TO NOW-0 MINUTES]WHERE { ?WaveObs a ssn:Observation; Aggregates ssn:observationResult ?windspeed; ssn:observedProperty sweetSpeed:WindSpeed.Static & Streaming ?TideObs a ssn:Observation; ssn:observationResult ?tidespeed; ssn:observedProperty sweetSpeed:TideSpeed. WindowsFILTER (?tidespeed<?windspeed)} Filters, Functions Disclaimer: some features NYI In progress: Benchmarking 41
  • Queries to Sensor DataSNEEqlRSTREAM SELECT id, speed, direction FROM wind [NOW]; Data Stream Management SystemEsper QLSELECT wind_speed FROM wind_sensor.win:time(10 min) Complex Event ProcessorsGSN RESTful servicehttp://montblanc.slf.ch:22001/multidata?vs[0]=wind_sensor&field[0]=wind_speed&from=15/09/2011+05:00:00&to=15/09/2011+15:00:00Pachube RESTful servicehttp://api.pachube.com/v2/feeds/14321/datastreams/4?start=2011-09-02T14:01:46Z&end=2011-09-02T17:01:46Z Sensor Data Middleware Querying through ontologies? 43
  • Differences among all these systems• Different Query Expressivity • Windows? • Union, Filters, Joins? • Aggregates, Groups?• Different delivery & query mechanisms • Pull, Push? • Continuous queries?, One-off?• How to merge streaming and static data queries• Commonalities in RESTful Services 44
  • Where is the Data? wan7 timed sp_wind timed: datetime PK 1 3.4Esper sp_wind: float 2 5.6 3 11.2GSN 4 1.2 5 3.1SNEE .. … SELECT sp_wind FROM wan7 WHERE sp_wind >10 SELECT sp_wind FROM wan7 [NOW -5 HOUR] WHERE sp_wind >10 45
  • Where is the Data?GSN server instance .. wan7 sensor1 sensor2 timed: datetime PK Esper sensor3 sp_wind: float … Mappings ssn:Observation 46
  • Creating Mappings ssn:observedProperty ssn:Observation ssn:Property http://swissex.ch/data# ssn:observationResult Wan7/WindSpeed/Observation{timed} sweetSpeed:WindSpeed wan7 ssn:SensorOutputtimed: datetime PK http://swissex.ch/data#sp_wind: float ssn:hasValue Wan7/ WindSpeed/ ObsOutput{timed} ssn:ObservationValue http://swissex.ch/data# qudt:numericValue Wan7/WindSpeed/ObsValue{timed} xsd:decimal sp_wind 47
  • R2RML• RDB2RDF W3C Group, R2RML Mapping language: • http://www.w3.org/2001/sw/rdb2rdf/r2rml/ :Wan4WindSpeed a rr:TriplesMapClass; rr:tableName "wan7"; rr:subjectMap [ rr:template "http://swissex.ch/ns#WindSpeed/Wan7/{timed}"; rr:class ssn:ObservationValue; rr:graph ssg:swissexsnow.srdf ]; rr:predicateObjectMap [ rr:predicateMap [ rr:predicate ssn: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" 48
  • Querying the Observations SELECT ?waveheight FROM STREAM <www.ssg4env.eu/SensorReadings.srdf> [NOW -10 MINUTES TO NOW STEP 1 MINUTE] WHERE { ?WaveObs a sea:WaveHeightObservation; :22001/ multidata ?vs [0]= wan7 & http://montblanc.slf.ch field [0]= sp_wind sea:hasValue ?waveheight; } Query:Wan4WindSpeed a rr:TriplesMapClass; Rewriting GSN rr:tableName "wan7"; SPARQLStream API rr:subjectMap [ rr:template"http://swissex.ch/ns#WindSpeed/Wan7/{timed}";Client rr:class ssn:ObservationValue; rr:graphssg:swissexsnow.srdf ]; Mappings Query rr:predicateObjectMap [ rr:predicateMap [ Processingrr:predicate ssn:hasQuantityValue ]; Sensor rr:objectMap[ rr:column "sp_wind" ] ]; Network [tuples] Data [triples] translation R2RML Mappings 49
  • Rewriting to different technologies SELECT ?windspeed FROM NAMED STREAM <http://swiss- experiment.ch/data#WannengratSensors.srdf> [NOW-10 MINUTE TO NOW-0 MINUTE] WHERE { Query ?WaveObs a ssn:Observation; Rewriting ssn:observationResult ?windspeed; Algebra ssn:observedProperty sweetSpeed:WindSpeed. } representationSELECT wind_speed_scalar_av, timed FROMwan7.win:time(10 min) Esper (CEP) SELECT wan7.wind_speed_scalar_av AS windspeed, wan7.timed AS windts FROM wan7[FROM NOW-10 MINUTES TO NOW] SNEE (DSMS) http://montblanc.slf.ch:22001/multidata?vs[0]=wan7& field[0]=wind_speed_scalar_av& from=15/05/2011+05:00:00&to=15/05/2011+15:00:00 GSN (Middleware) http://api.pachube.com/v2/feeds/14321/datastreams/4?st art=2011-09-02T14:01:46Z&end=2011-09-02T17:01:46Z Pachube (Middleware) 50
  • Query rewriting SELECT ?waveheight FROM STREAM <www.ssg4env.eu/SensorReadings.srdf> [NOW – 5 HOUR TO NOW] WHERE { ?WaveObs a ssn:ObservationValue; qudt:numericalValue ?waveheight; FILTER (?waveheight>10) }SELECT sp_wind FROM wan7 [NOW -5 HOUR] WHERE sp_wind >10
  • Also for RESTful services• GSN Web Services• GSN URL API • Compose the query as a URL: 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 ? 52
  • Algebra expressionsπ timed, 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& sp_wind c_vs [0]= wan7 & c_field [0]= sp_wind & c_min [0]=10σ sp_wind>10ω 5 Hour SELECT sp_wind FROM wan7 [NOW -5 HOUR] WHERE sp_wind >10wan7 53
  • Using the Mappings π timed, sp_windSELECT ?waveheight σFROM STREAM <www.ssg4env.eu/SensorReadings.srdf>[NOW – 5 HOUR TO NOW] sp_wind>10WHERE { ?WaveObs a ssn:ObservationValue; qudt:numericalValue ?waveheight; ω 5 Hour FILTER (?waveheight>10) } wan7 wan7 ssn:ObservationValue http://swissex.ch/data# timed: datetime PK qudt:numericalValue Wan7/WindSpeed/ObsValue{timed} sp_wind: float xsd:datatype sp_wind 54
  • Algebra construction π timed, sp_windwindsensor1windsensor2 σ sp_wind>10 ω 5 Hour wan7 55
  • Static optimizationπ timed, π timed, π timed, sp_wind windvalue windvalueσ sp_wind>10 σ windvalue>10 σ windvalue>10ω 5 Hour ω 5 Hour ω 5 Hourwan7 windsensor1 windsensor2 56
  • The whole picture Application Middleware SPARQL-STR CQELS REST SPARQL Linked Streams Linked DataData Stream COAP VirtualManagement Sensors Systems Sensors
  • In Summary, and for the future• Use ontologies to query sensor data • Using R2RML mappings (as in what I have been presenting) • Using native RDF streams (as what Manfred presented on Tuesday)• Use extensions to SPARQL to handle data streams • However, there is a need to standardise all these query languages • There is also a need for good benchmarks• Different underlying systems can provide support to data streams • DSMS, CEP, RESTful services • What is the best one for each purpose? Again, we need to have good benchmarks 60
  • Some references• Sheth A, Henson C, and Sahoo S, Semantic Sensor Web, IEEE Internet Computing, 2008.• Sequeda J, Corcho O. Linked Stream Data: A Position Paper. Proceedings of the 2nd International Workshop on Semantic Sensor Networks, 2009.• 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, 2010.• García-Castro R, Corcho O. Five challenges for the Semantic Sensor Web. Semantic Web Journal 1, 2011• García-Castro R, Corcho O, Hill C. A Core Ontological Model for Semantic Sensor Web Infrastructures. International Journal on Semantic Web and Information Systems Special Issue on Sensor Networks, Internet of Things and Machine-to-Machine Communications, Volume 8, Issue 1, 2013• Calbimonte JP, Jeung H, Corcho O, Aberer K. Enabling Query Technologies for the Semantic Sensor Web. International Journal on Semantic Web and Information Systems Special Issue on Sensor Networks, Internet of Things and Machine-to-Machine Communications, Volume 8, Issue 1, 2013
  • SSSW2012 – The Ninth Summer School on Ontological Engineering and Semantic WebSemantic Sensor Networks(and Social) Semantic Web Oscar Corcho (with the help of Jean Paul Calbimonte) Ontology Engineering Group Facultad de Informática, Universidad Politécnica de Madrid ocorcho@fi.upm.es
  • Instructions for hands-on session• It will be focused on understanding how sensor data can be retrieved • Disclaimer: a bit of cheating (working on historical data from weather stations in Spain, and no continuous SPARQL querying) • For those interested in hardcore stuff, talk to me later• Quick instructions • Based on the material that you already used for session 2 • Remember: Javascript, SPARQL queries, etc. • Download a zip file from http://bit.ly/LHsqLx • In fact, you will find it already in your desktop as sssw12-ho- s7.zip • Unzip it into /var/www (remember ‘sudoing’ and ‘chowning’ or ‘chmoding’) • Go to http://localhost/sssw12-ho-s7/s7.html (instructions) • The results will be at http://localhost/sssw12-ho-s7/index.html 63