UPM – Project Meeting Innsbruck - Feb/March 2011

Slide 2 of x WP1 – D1.1 - TOC 1. Introduction (UPM) 2. Characterization Mechanisms for Single- Modality Unknown or Changing Data Sources 3. Characterization Mechanisms for Multi- Modality Combinations of Unknown or Changing Data Sources 4. Conclusion (UPM) 6/2/2015

Slide 3 of x WP1 – D1.1 – Single Modality 1. Characterization Mechanisms for Single- Modality Unknown or Changing Data Sources ◦1.1 Sensor Data Streams: Semantic Model Generation (UPM + JSI + EPFL)  Obtain domain ontologies based on metadata about sensors + numeric values obtained from them --- UPM  Relate these sensor data sources with other external knowledge sources (e.g., from tags to Cyc/DBPedia) --- JSI  In all cases, overarching usage of SSN Ontology for sensor sources description (demonstrated with various platforms, like GSN, Pachube, ad-hoc deployments, etc.) --- JSI, EPFL, UPM ◦1.2 Text Streams: data analysis (JSI)  e.g., Twitter 6/2/2015

Slide 4 of x WP1 – D1.1 – Multi Modality 2. Characterization Mechanisms for Multi- Modality Combinations of Unknown or Changing Data Sources ◦2.1 Near Real Time Mapping between Textual Sources and Social Networks (JSI) ◦2.2 Ontology-based Data Integration for Multi- Modality Data Sources (UPM)  Integration of sensors with relational databases (e.g., current continuous temperature values with average (normal) values for a region) 6/2/2015

Slide 5 of x WP1 – Work on top of GSN Download-based data access ◦Add SPARQL support Wiki-based metadata repository ◦Add Search feature ◦SSN Ontology will probably play its part here Distributed GSN instances environment ◦Add Ontology-Based Data Integration 6/2/2015

Slide 6 of x Background Previous work at UPM: ◦Mapping data streams to ontologies ◦Use ontological schemas to write queries over streaming data sources ◦Rewriting SPARQL-Stream queries into declarative stream queries (e.g. SNEEql) ◦Experience in Flood environmental sensor data. 6 Calbimonte, J-P., Corcho O., Gray, A. Enabling Ontology-based Access to Streaming Data Sources. In ISWC 2010.

Slide 7 of x Ontology-based Data Access Query translation Query Evaluator Client Stream-to-Ontology mappings 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

Slide 8 of x EPFL GSN Deployment for SwissEx Distributed environment: GSN Davos, GSN Zurich, etc. ◦In each site, a number of sensors available ◦Each one with different schema ◦However overlapping concepts in the schemas, e.g. temperature ◦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,

Slide 9 of x Initial look at GSN SwissEx data Mirror data available. Web service interface: planetdata.epfl.ch:22001/services/GSNWe bService?wsdl ◦ListVirtualSensorNames  wannengrat_gupf_unten  wannengrat_unterhalb_felsen  wan2  wan_sen7_2008  wan_sen4_2008  etc Each one has a schema (attributes and types, etc) 9

Slide 10 of x GSN getting data getMultiData ◦Can request for a specific virtual sensor ◦Can request data from ALL sensors ◦Queries configured (can add new queries as well) Sample query: ◦select pk, air_temperature, relative_humidity, incoming_shortwave_radiation, outgoing_shortwave_radiation, net_shortwave_radiation, wind_speed_50cm, wind_speed_100cm, wind_speed_200cm, wind_speed_max_50cm, wind_speed_max_100cm, wind_speed_max_200cm, wind_direction, precipitation, battery_voltage, timed from wannengrat_tib3 where timed > and timed <= and pk < order by timed desc (size: 20 offset: 0) 10

Slide 11 of x GSN getting data The web service parameters allow basic query configuration: ◦Include all/some fields (fieldNames) ◦Add basic selection conditions ◦Add aggregations ◦Indicate lower/upper time (time-based selection) 11 Enabling Semantic Integration of Streaming Data Sources

Slide 12 of x GSN getting data Example GetMultiData request = new GetMultiData(); GSNWebService_FieldSelector[] selector = new GSNWebService_FieldSelector[1]; selector[0]= new GSNWebService_FieldSelector(); selector[0].setFieldNames(new String[]{"air_temperature"});//include only this field selector[0].setVsname("ALL");//include data from all sensors request.setFieldSelector(selector); request.setTimeFormat("unix"); request.setNb(20);//maximum 20 results request.setFrom( l);//lower bound time epoch request.setTo(System.currentTimeMillis());//upper bound GetMultiDataResponse response = gsn.getMultiData(request ); //call service 12

Slide 13 of x Idea Add Ontology-based distributed query processing: ◦provide me all temperature sensors that have shown higher than 30 degrees ◦Use ontological schemas fro queries, internally map to the appropriate sensors ◦Query rewritten and dispatched to the appropriate GSN instances ◦Return query results URL GSN Davos GSN Zurich GSN Chur Wiki Metadata query Distributed QP

Slide 14 of x WP1 – D Semantic Model Generation from Sensor Data Streams (UPM) 6/2/2015

Slide 15 of x WP1 – D Usage of SSN Ontology for sensor sources description (JSI,EPFL, UPM) See Contribution plan on WP 2 (Work on top of GSN) 6/2/2015

Slide 16 of x WP1 – SSN Ontology Status : Stable ◦ mages/3/36/Ssn.xmlhttp:// mages/3/36/Ssn.xml Usage : 1.Data Discovery and Linking 2.Device Discovery and Selection 3.Provenance and Diagnosis 4.Device Operation Tasking and Programming ◦ eport_Motivating_Use_cases#Use_caseshttp:// eport_Motivating_Use_cases#Use_cases 6/2/2015

Slide 17 of x Ontologies overview SWEET Service Coastal Defences Ordnanc e Survey Addition al Regions Role DOLCE UltraLite Schema FOAF Upper External SSG4Env infrastructure Flood domain 17 SSN

Slide 18 of x Ontologies Infrastucture ◦Core sensor network ontology ◦Service and schema ontologies Domain ◦Flood use case ontology network 18

Slide 19 of x Ontology module Class Individual Subclass-of property Type property Object or datatype property Equivalent to a restriction in an object property Subclass of a restriction in an object property Legend Module Class = objectProperty only | some objectProperty only | some property Class Individual

Slide 20 of x Skeleton Device Deployment PlatformSite System Process ConstraintBlockMeasuringCapability OperatingRestriction Data Overview of the SSN ontology modules

Slide 21 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

Slide 22 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

Slide 23 of x Data Device Deployment PlatformSite System DeploymentRelated Process Deployment Platform Device Sensor SensingDevice Sensing SensorInput ObservationValue SensorOutput Process Skeleton Observation Property FeatureOfInterest DOLCE UltraLite SituationMethod Region Object Event QualityEvent InformationObject PhysicalObject Process DesignedArtifact or Alignment to DOLCE UltraLite

Slide 24 of x Ontologies Infrastucture ◦Core sensor network ontology ◦Service and schema ontologies Domain ◦Flood use case ontology network 24

Slide 25 of x Service ontology coversRegion hasTemporalExtent hasSpatialExtent hasDataset hasInterface hasServiceType containsOperationhasParameter includesProperty includesFeature hasEndpointReference 25 hasSchema hasStyleURL WebService StatefulWebService xsd:string sw:Dataset sw:Regionsw:SpatialExtent sw:TemporalExtent ssn:Property ssn:FeatureOfInterest sm:Schema xsd:anyURI InterfaceOperationParameter DataAccessInterface… ServiceType OGCS.T.SSG4EnvS.T.GeoJSONS.T.XMLS.T.RSSXMLS.T. Schema Metadata SSN SWEETXSD ISO 19119

Slide 26 of x Schema Metadata ontology hasExtent hasPrimaryKey hasAttribute or hasSQLType hasTimestampAttribute 26 equivalentToProperty Extent RelationStream Schema DatabaseSchemaDataStreamSchema PrimaryKey Attribute TimestampAttribute ssn:Property SQLType SSN

Slide 27 of x Ontologies Infrastucture ◦Core sensor network ontology ◦Service and schema ontologies Domain ◦Flood use case ontology network 27

Slide 28 of x Coastal Defences ontology locatedInRegionssn:hasProperty hasOceanRegionProperty 28 ssn:Propertyssn:FeatureOfInterestsw:Region AssetPropertyOceanRegionProperty Assetos:TopographicObject OceanRegion… … TideHeightWaveHeight SSNSWEET OS

Slide 29 of x Features and properties Physical atmosphere Air temperature Wind speed Wind direction Visibility Asset Height Condition Class Width Inspection date Maintainer Location Mastermap Id Flood plain Water depth Flood zone Flood zone type Flood defence policy Strategic defence option Ocean region Wave height Tide height Vessel Location Name Bearing Type Size Callsign Speed ETA Road problem Location Road identifier Description Event time 29

Slide 30 of x Additional Regions ontology Coastal Defence Partnership Coastal Defence Partnership (Modelled area) Solent Solent (Modelled area) South East England South East England (BRANCH) South East England (CCO) Southern Coastal England (CCO) Solent (AIS live) South East England (Highways Agency) South West England (Highways Agency) 30 sw:Region gz:NamedPlace

Slide 31 of x Role ontology hasRegionOfResponsibility hasResponsibility undertakesTask foaf:member 31 assumesRole hasPosition occupies hasSubOrganization ssn:hasProperty hasRelatedProperty hasRelatedFeature isFulfilledBy defines isAssignedTo appliesTo operatesWithin ssn:Property ssn:FeatureOfInterest Position RoleTask Responsibility Duty foaf:Person foaf:Organization sw:Region SSN SWEET FOAF

Slide 32 of x WP1– S2O Mappings ◦Extension of R2O Result ◦SPARQL bound variables in XML format 6/2/2015

Slide 33 of x WP2 – Contribution Plan Transformation tools for sensors platform(eg: Pachube) description to SSN Ontology ◦To start in Spring 2011 Tools for transforming geography objects (GML, Oracle Spatial, etc) to RDF ◦ ◦Geometry2rdf presentation for more detail 6/2/2015

Slide 34 of x WP2 – Contribution Plan Tools for transforming geography objects (GML, Oracle Spatial, etc) to RDF ◦ ◦Geometry2rdf presentation for more detail 6/2/2015

Slide 35 of x WP4 – PlanetData Vocabulary Document uploaded on wiki WP4 page (Jan 2011) ◦Feedback and comments? Analysis of existing vocabularies ◦Identify prominent features of analyzed vocabularies General Purpose Vocabularies ◦DC Dataset Purpose Vocabularies ◦Dcat ◦Void Stream Purpose Vocabularies ◦Pachube Information Model ◦Sstream ◦SSN 6/2/2015

Slide 36 of x WP4 – PlanetData Vocabulary Some Examples - SStream Pollution sensor measuring CO2 emission ◦Continuous ◦Valid for 1 hour :COSensor1 a ss:Sensor. :COSensor1Stream a ss:ContinuosStream; ss:expires P1H; contains :COSensor1StreamContent. 6/2/2015

Slide 37 of x WP4 – PlanetData Vocabulary Some Examples - Pachube Not clearly defined ◦Based on its API Environment properties ◦Static vs dynamic ◦Outdoor vs indoor ◦Live vs frozen : MadridStreet1 a pachube:Environment; pachube:location_lat 40.26; pachube:location_long 3.42; pachube:environment_disposition "Static"; pachube:environment_exposure "Outdoor"; pachube:environment_domain "Physical"; pachube:feed_status "Live"; pachube:hasDatastream :COSensor1. :COSensor1 a pachube:DataStream. 6/2/2015

Slide 38 of x WP4 – PlanetData Vocabulary Some Examples – Sstream+Pachube Sensor on Fred ◦Heart rate ◦Private data ◦Restriction to hospital ◦Every 5 minutes :Fred a foaf:Person; ss:sensor :HeartSensor. :HeartSensor a ss:Sensor; ss:attachedTo :Fred. dc:description "Heart rate sensor". dc:publisher _:bnode1. _:bnode1 a ss:PeriodicStream; ss:publishedBy :HeartSensor. ss:dataType xs:decimal. ss:contains _:bnode2. dc:accrualPeriodicity _:bnode3. _:bnode2 a ss:StreamContent; rdfs:about ; pachube:ip_restriction :HOSPITAL_IP. _:bnode3 a ss:Frequency;} ss:duration P0Y0M0DT0H5M. 6/2/2015

Slide 39 of x WP4 – PlanetData vocabulary Some Proposals Dynamic dataset properties ◦Expiration properties ◦Continuous/periodic ◦Feed status (live/frozen) Subjective/Objective properties ◦dcat:dataQuality ◦ssn:precision Location properties beyond standard geographic features ◦mobile or static location ◦indoor or outdoor Access Control ◦Public sensors: pollution sensors ◦Private sensors: heart-rate sensors ◦Allowed/blocked sources ◦Access duration ◦Frequency limit 6/2/2015

Slide 40 of x WP4 – Relevant Data sets Channel Coastal Observatory ◦Pending final confirmation that it can be used AEMET (Agencia Española de Meteorología) ◦Currently working on its transformation. Demonstrator soon InfoTerre : In/Out? PSA : In/Out? Sensorbase : In/Out 6/2/2015

Slide 41 of x WP6 - curriculum Motivation ◦Comparison with Relational DB storage Streaming data models ◦Unbounded streams ◦Tuples, Windows ◦Timestamps ◦K-constraints Query Languages ◦Relational operatorsWindow operators, temporal operators ◦Aggregators ◦Joins Semantic streaming data ◦RDF Stream data models ◦SPARQL extensions for RDF Streams ◦Reasoning with Streams ◦Complex event processing ◦Linked Streaming Data Query processing ◦Continuous queries ◦Window evaluation ◦Aggregates evaluation, approximative queries ◦Static optimization ◦Query optimization, statistics ◦Load shedding ◦Sampling 6/2/2015