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Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
Representing and Querying Geospatial Information in the Semantic Web
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Representing and Querying Geospatial Information in the Semantic Web

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  • 1. Representing and QueryingGeospatial Information in theSemantic WebDept. of Informatics and Telecommunications,National and Kapodistrian University of Athens, GreeceKostis Kyzirakos
  • 2. Outline• Introduction• The data model stRDF• The query language stSPARQLand a comparison to GeoSPARQL• The system Strabon forstSPARQL and GeoSPARQL• Experimental Evaluation• Real Time Fire Monitoring• Conclusions
  • 3. Main ideaHow do we represent and query geospatialinformation in the Semantic Web? Develop appropriate vocabularies andontologies Extend RDF to take into account thegeospatial dimension Extend SPARQL to query the new kinds ofdata Use Open Geospatial Consortium (OGC) andother geospatial industry standards
  • 4. W3C Basic Geo Vocabulary
  • 5. The data modelstRDFIntroductionThe data modelstRDFThe query languagestSPARQLThe system StrabonExperimentalEvaluationReal Time FireMonitoringConclusions
  • 6. The Data Model stRDF• stRDF extends RDF with:• Spatial literals encoded by Boolean combinations oflinear constraints• New datatype for spatial literals (strdf:geometry)• Valid time of triples encoded by Booleancombinations of temporal constraints• stRDF (most recent version)• Spatial literals encoded in Well-Known Text/GML(OGC standards)• Valid time of triples ignored for the time being
  • 7. 7Burnt Area Products
  • 8. "POLYGON(( 38.16 23.7, 38.18 23.7, 38.1823.8, ... 38.1623.8, 38.16 3.7));<http://spatialreference.org/ref/epsg/4121/>"^^strdf:WKTSpatial Literal(OpenGIS SimpleFeatures)Spatial Data TypeWell-Known Text“1” ^^xsd:int"23.7636"^^xsd:doublenoa:hasAreanoa:hasIDrdf:typeex:BurntArea1noa:BurntAreageo:geometryBurnt Area Products
  • 9. strdf:geometry rdf:type rdfs:Datatype;rdfs:subClassOf rdfs:Literal.strdf:WKT rdf:type rdfs:Datatype;rdfs:subClassOf strdf:geometry.strdf:GML rdf:type rdfs:Datatype;rdfs:subClassOf strdf:geometry.The stRDF Data Model
  • 10. The querylanguagestSPARQLIntroductionThe data modelstRDFThe query languagestSPARQLThe system StrabonExperimentalEvaluationReal Time FireMonitoringConclusions
  • 11. We define a SPARQL extension function for each functiondefined in the OpenGIS Simple Features Access standard Basic functions Get a property of a geometry (e.g., strdf:srid) Get the desired representation of a geometry (e.g., strdf:AsText) Test whether a certain condition holds (e.g., strdf:IsEmpty,strdf:IsSimple) Functions for testing topological spatial relationships(e.g., strdf:equals, strdf:intersects) Spatial analysis functions Construct new geometric objects from existing geometric objects (e.g.,strdf:buffer, strdf:intersection, strdf:convexHull) Spatial metric functions (e.g., strdf:distance, strdf:area) Spatial aggregate functions (e.g., strdf:union, strdf:extent)stSPARQL: Geospatial SPARQL 1.1
  • 12. Select clause Construction of new geometries (e.g., strdf:buffer(?geo, 0.1)) Spatial aggregate functions (e.g., strdf:union(?geo)) Metric functions (e.g., strdf:area(?geo))Filter clause Functions for testing topological relationships between spatial terms (e.g.,strdf:contains(?G1, strdf:union(?G2, ?G3))) Numeric expressions involving spatial metric functions(e.g., strdf:area(?G1) ≤ 2*strdf:area(?G2)) Boolean combinationsHaving clause Boolean expressions involving spatial aggregate functions and spatial metricfunctions or functions testing for topological relationships between spatialterms (e.g., strdf:area(strdf:union(?geo))>1)UpdatesstSPARQL: Geospatial SPARQL 1.1
  • 13. SELECT ?forest ?burntAreaWHERE {?burntArea rdf:type noa:BurntArea;noa:hasGeometry ?baGeom.?forest rdf:type noa:Region;clc:hasLandCover noa:coniferousForest;clc:hasGeometry ?fGeom.FILTER(strdf:intersects(?baGeom,?fGeom))}Find coniferous forests that have been affected byfiresstSPARQL: An example (1/2)SpatialFunction
  • 14. SELECT ?burntArea(strdf:intersection(?baGeom,strdf:union(?fGeom))AS ?burntForest)WHERE {?burntArea rdf:type noa:BurntArea;noa:hasGeometry ?baGeom.?forest rdf:type noa:Region;clc:hasLandCover noa:coniferousForest;clc:hasGeometry ?fGeom.FILTER(strdf:intersects(?baGeom,?fGeom))}GROUP BY ?burntArea ?baGeomIsolate the parts of the burnt areas that lie inconiferous forests. SpatialAggregatestSPARQL: An example (2/2)
  • 15. CoreTopology VocabularyExtension- relation familyGeometry Extension- serialization- versionGeometry TopologyExtension- serialization- version- relation familyQuery RewriteExtension- serialization- version- relation familyRDFS EntailmentExtension- serialization- version- relation familyParameters• Serialization• WKT• GML• Relation Family• SimpleFeatures• RCC-8• EgenhoferThe OGC Standard GeoSPARQL
  • 16. GeoSPARQL is a recently completed OGC standardstSPARQL and GeoSPARQL have been developed independentlyFunctionalities similar to stSPARQL: Geometries are represented using literals similarly to stSPARQL. The same families of functions are offered for querying geometries.Functionalities beyond stSPARQL: Topological relations can now be asserted as well so thatreasoning and querying on them is possible.Strabon supports both stSPARQL and GeoSPARQLstSPARQL and GeoSPARQL
  • 17. The systemStrabonstrabon.di.uoa.grIntroductionThe data modelstRDFThe query languagestSPARQLThe system StrabonExperimentalEvaluationReal Time FireMonitoringConclusions
  • 18. Strabon ArchitecturestRDFgraphsstSPARQL/GeoSPARQLqueriesWKT GML
  • 19. DictionaryTriplesStorage Schemeex:sensor1 rdf:type ex:Sensor .ex:sensor1 ex:measures ex:Temperature .ex:sensor1 ex:hasLocation ex:location1 .ex:location1 strdf:hasSpatialExtent "POINT(37.94194 23.63722)<http://srid.org/ref/epsg/4326/>"ˆˆogc:WKT .SUBJECT PREDICATE OBJECTex:sensor1 rdf:type ex:Sensorex:sensor1 ex:measures ex:Temperatureex:sensor1 ex:hasLocation ex:location1ex:location1 strdf:hasSpatialExtent "POINT(37.94194 23.63722)<http://srid.org/ref/epsg/4326/>"ˆˆogc:WKTTriplesSUBJECTPREDICATE OBJECT1 2 31 4 51 6 77 8 9ID VALUE1 ex:sensor12 rdf:type3 ex:Sensor4 ex:measures5 ex:Temperature6 ex:hasLocation7 ex:location18 strdf:hasSpatialExtent9 "POINT(37.9419423.63722)<http://srid.org/ref/epsg/4326/>" ˆˆogc:WKTtype_2SUBJECT OBJECT1 3measures_4SUBJECT OBJECT1 5hasLocation_6SUBJECT OBJECT1 7hasSpatialExtent_8SUBJECT OBJECT1 9uri_valuesID VALUE1 ex:sensor12 rdf:type3 ex:Sensor4 ex:measures5 ex:Temperature6 ex:hasLocation7 ex:location18 strdf:hasSpatialExtentlabel_valuesID VALUE9 POINT(37.94194 23.63722)<http://srid.org/ref/epsg/4326/>datatype_valuesID VALUE9 ogc:WKTgeo_valuesID VALUE9
  • 20. Query ProcessingstSPARQL/GeoSPARQLquery• Parser generatesabstract syntax tree• Abstract syntax treemapped to internalalgebra of Sesame• Standard optimizationsperformed• Evaluator producescorresponding SQLquery• DBMS evaluates theSQL query• Post-processing
  • 21. Query Processing (cont’d)• Deviate from the evaluation strategy of Sesamefor SPARQL extension functions• Push the evaluation of extension functions tounderlying DBMS• Spatial predicates evaluated by PostGIS• Spatial joins now affect query plan• Avoid Cartesian products• Results may be returned in well-known industryformats• KML/KMZ• GeoJSON• GML
  • 22. ExperimentalEvaluationIntroductionThe data modelstRDFThe query languagestSPARQLThe system StrabonExperimentalEvaluationReal Time FireMonitoringConclusions
  • 23. Experimental Evaluation• Goal: Evaluate the performance ofStrabon vs other systems• Real workload based on geospatiallinked datasets150 million triples• Synthetic workloadHalf a billion triples
  • 24. Strabon vs other systems• Strabon over PostgreSQL(Strabon-PG)• Strabon over System X(Strabon-X)• Implementation over RDF-3X (Brodtet. al)• Parliament (BBN Technologies)• Openlink Virtuoso• Naive Implementation over Sesame
  • 25. Real world workload: DataDBpedia GeoNames LGD Pachube SwissEx CLC GADMSize 7.1GB 2.1GB 6.6GB 828KB 33MB 14GB 146MBTriples 58,722,893 17,688,602 46,296,978 6,333 277,919 19,711,926 255SpatialTerms386,205 1,262,356 5,414,032 101 687 2,190,214 51DistinctSpatialTerms375,087 1,099,964 5,035,981 70 623 2,190,214 51Points 375,087 1,099,964 3,205,015 70 623 - -Linestrings - - 353,714 - - - -Polygons - - 1,704,650 - - 2,190,214 51( )79,831vertices
  • 26. Real world workload: DataDBpedia GeoNames LGD Pachube SwissEx CLC GADMSize 7.1GB 2.1GB 6.6GB 828KB 33MB 14GB 146MBTriples 58,722,893 17,688,602 46,296,978 6,333 277,919 19,711,926 255SpatialTerms386,205 1,262,356 5,414,032 101 687 2,190,214 51DistinctSpatialTerms375,087 1,099,964 5,035,981 70 623 2,190,214 51Points 375,087 1,099,964 3,205,015 70 623 - -Linestrings - - 353,714 - - - -Polygons - - 1,704,650 - - 2,190,214 51( )79,831vertices1
  • 27. Real world workload: Queries
  • 28. Real world workload: ResultsCacheStateSystem Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8Cold(sec.)Naive 0.08 1.65 >8h 28.88 89 170 844 1.699StrabonPG2.01 6.79 41.39 10.11 78.69 60.25 9.23 702.55StrabonX1.74 3.05 1623 46.52 12.57 2409 >8h 57.83Parliament 2.12 6.46 >8h 229.72 1130 872 3627 3786Warm(sec.)Naïve 0.01 0.03 >8h 0.79 43.07 88 708 1712StrabonPG0.01 0.81 0.96 1.66 38.74 1.22 2.92 648.1StrabonX0.01 0.26 1604.9 35.59 0.18 3196 >8h 44.72Parliament 0.01 0.04 >8h 10.91 358.92 483.29 2771 350228
  • 29. Synthetic Workload• Workload based on a synthetic datasetDataset based on OpenStreetMaps10 million triples (2 GB) up to half abillion triples (50GB) Implemented custom bulk loaderTriples with spatial literals: 1 up to 46million triples• Response time of queries with variousthematic and spatial selectivities
  • 30. Synthetic Workload: SampleData
  • 31. Synthetic Workload:Sample stSPARQL QuerySELECT *WHERE {?tag geordf:key "1" .?node geordf:hasTag ?tag .?node geo:hasGeography1 ?geo .FILTER (strdf:inside(?geo,"POLYGON((-1 -1, 0.056568542 -1,0.056568542 0.056568542,-1 0.056568542, -1 -1))"^^strdf:WKT))}
  • 32. Real-world Workload:100 million triples – warm cachesTag 1 Tag 1024number of Nodes in query regionnumber of Nodes in query regionResponsetime(sec)Responsetime(sec)
  • 33. Real-world Workload:100 million triples – cold cachesTag 1 Tag 1024number of Nodes in query region number of Nodes in query regionResponsetime(sec)
  • 34. 500 million triples – tagscomparisonResponsetime(sec)
  • 35. Real-world Workload:Query Plans
  • 36. Real-world Workload:100 million triples – cold cachesTag 1 Tag 1024number of Nodes in query region number of Nodes in query regionResponsetime(sec)
  • 37. Findings• Strabon over PostgreSQL outperformsother systems in case of warm caches• Results in case of cold caches mixed• PostreSQL optimizer needs to take intoaccount spatial selectivity• PostGIS 2.0 moves towards it
  • 38. System Language Index Geometries CRS support GeospatialFunctionSupportStrabon stSPARQL/GeoSPARQL*R-tree-over-GiSTWKT / GMLsupportYes • OGC-SFA• Egenhofer• RCC-8Parliament GeoSPARQL* R-Tree WKT / GMLsupportYes •OGC-SFA•Egenhofer•RCC-8Oracle GeoSPARQL? R-Tree,QuadtreeWKT / GMLsupportYes •OGC-SFA•Egenhofer•RCC-8Brodt et al.(RDF-3X)SPARQL R-Tree WKT support No OGC-SFAPerry SPARQL-ST R-Tree GeoRSS GML Yes RCC-8AllegroGraph ExtendedSPARQLDistributionsweepingtechnique2D pointgeometriesPartial •Buffer•Bounding Box•DistanceOWLIM ExtendedSPARQLCustom 2D pointgeometriesNo •Point-in-polygon•Buffer•DistanceVirtuoso SPARQL R-Tree 2D pointgeometriesYes SQL/MM (subset)uSeekM SPARQL R-tree-over WKT support No OGC-SFA
  • 39. Real TimeFire Monitoringpapos.space.noa.gr/fend_staticIntroductionThe data modelstRDFThe query languagestSPARQLThe system StrabonExperimentalEvaluationReal Time FireMonitoringConclusions
  • 40. Wildfire Monitoring and Burnt AreaMapping (NOA)
  • 41. Cataloguing Service& Metadata CreationProcessing Chain(SciQL based)This image cannot currently be displayed.HotSpotsBack End: MonetDB / StrabonFire monitoring applicationData VaultEumetsat @ 9.5EastRaw Data• Corine Landcover• Admin Boundaries• POIsExternalSourcesFront End: GUIMap Element• Search & Display• Search for raw & processed• Real-time Fire Monitoring• Refinement (Post-Processing)• Linked DataGeospatialOntologyWeb accessbased on SemanticsLinkedGeospatial DataSemantictechnologies
  • 42. High Level Data Modeling Need for representing Standard product metadata Standard product semanticannotations Geospatial information Temporal information Need to link to other data sources GIS data Other information on the Web
  • 43.  Improving the fire monitoring service usingSemantic Web technologies Representing fire related products usingontologies Enriching products with linked geospatial data Improving accuracy with respect to: Underlying land cover/land use Persistence in timehttp://papos.space.noa.gr/fend_static/Fire Monitoring Application
  • 44. NOA Ontology
  • 45.  Datasets that we developed and published aslinked data: Corine Land Use / Land Cover Coastline of Greece Greek Administrative Geography Portal: http://www.linkedopendata.gr/ Datasets from Linked Open Data Cloud LinkedGeoData GeoNamesLinked Geospatial Data
  • 46. Linked Open Data Cloud
  • 47. Linked Open Data
  • 48. Linked Open Data
  • 49. Linked Open Data
  • 50. Linked Open Data
  • 51. Using ontologies and stRDF to modelknowledge extracted from satellite images,metadata of satellite images and auxiliarygeospatial data can improve tasks like: Generated maps combining diverseinformation sources Increase hotspot accuracy correlatingthem with auxiliary dataImprovements
  • 52. Get all hotspots detected in Peloponnese on24/08/2007.SELECT ?h ?hGeo ?hAcqTime ?hConfidence ?hConfirmation ?hProvider?hSensor ?hSatelliteWHERE { ?h rdf:type noa:Hotspot ;noa:hasGeometry ?hGeo ;noa:hasAcquisitionTime ?hAcqTime ;noa:hasConfidence ?hConfidence ;noa:isProducedBy ?hProvider ;noa:hasConfirmation ?hConfirmation ;noa:isDerivedFromSensor ?hSensor ;noa:isDerivedFromSatellite ?hSatellite .FILTER("2007-08-24T00:00:00"^^xsd:dateTime <= ?hAcqTime &&?hAcqTime <= "2007-08-24T23:59:59"^^xsd:dateTime).FILTER(strdf:contains("POLYGON((21.027 38.36, 23.77 38.36,23.77 36.05, 21.027 36.05, 21.027 38.36))"^^strdf:WKT, ?hGeo) ) . }Discovering EO Data
  • 53. Get all hotspots detected in Peloponnese on24/08/2007.SELECT ?h ?hGeo ?hAcqTime ?hConfidence ?hConfirmation ?hProvider?hSensor ?hSatelliteWHERE { ?h rdf:type noa:Hotspot ;noa:hasGeometry ?hGeo ;noa:hasAcquisitionTime ?hAcqTime ;noa:hasConfidence ?hConfidence ;noa:isProducedBy ?hProvider ;noa:hasConfirmation ?hConfirmation ;noa:isDerivedFromSensor ?hSensor ;noa:isDerivedFromSatellite ?hSatellite .FILTER("2007-08-24T00:00:00"^^xsd:dateTime <= ?hAcqTime &&?hAcqTime <= "2007-08-24T23:59:59"^^xsd:dateTime).FILTER(strdf:contains("POLYGON((21.027 38.36, 23.77 38.36,23.77 36.05, 21.027 36.05, 21.027 38.36))"^^strdf:WKT, ?hGeo) ) . }Discovering EO Data
  • 54. Get all hotspots detected in Peloponnese on24/08/2007.SELECT ?h ?hGeo ?hAcqTime ?hConfidence ?hConfirmation ?hProvider?hSensor ?hSatelliteWHERE { ?h rdf:type noa:Hotspot ;noa:hasGeometry ?hGeo ;noa:hasAcquisitionTime ?hAcqTime ;noa:hasConfidence ?hConfidence ;noa:isProducedBy ?hProvider ;noa:hasConfirmation ?hConfirmation ;noa:isDerivedFromSensor ?hSensor ;noa:isDerivedFromSatellite ?hSatellite .FILTER("2007-08-24T00:00:00"^^xsd:dateTime <= ?hAcqTime &&?hAcqTime <= "2007-08-24T23:59:59"^^xsd:dateTime).FILTER(strdf:contains("POLYGON((21.027 38.36, 23.77 38.36,23.77 36.05, 21.027 36.05, 21.027 38.36))"^^strdf:WKT, ?hGeo) ) . }Discovering EO Data
  • 55. Get all coniferous forests in PeloponneseSELECT ?a ?aGeoWHERE{?a rdf:type clc:Area;clc:hasLandUse ?aLandUse;noa:hasGeometry ?aGeo.?aLandUse rdf:type ?aLandUseType.FILTER(?aLandUseType =clc:ConiferousForest).FILTER(strdf:contains("POLYGON((21.02738.36, 23.77 38.36, 23.77 36.05,21.027 36.05, 21.027 38.36))”^^strdf:WKT,?aGeo)).}Retrieving a Map Layer (1/3)
  • 56. Get all coniferous forests in PeloponneseSELECT ?a ?aGeoWHERE{?a rdf:type clc:Area;clc:hasLandUse ?aLandUse;noa:hasGeometry ?aGeo.?aLandUse rdf:type ?aLandUseType.FILTER(?aLandUseType =clc:ConiferousForest).FILTER(strdf:contains("POLYGON((21.02738.36, 23.77 38.36, 23.77 36.05,21.027 36.05, 21.027 38.36))”^^strdf:WKT,?aGeo)).}Retrieving a Map Layer (1/3)
  • 57. • Get all primary roads in PelloponneseSELECT ?r ?rGeoWHERE{ ?r a ?rType ;noa:hasGeometry ?rGeo .FILTER(?rType = lgdo:Primary) .FILTER(strdf:contains("POLYGON((21.027 38.36, 23.77 38.36,23.77 36.05, 21.027 36.05,21.027 38.36))"^^strdf:WKT,?rGeo) ).}Retrieving a map layer (2/3)
  • 58. • Get all primary roads in PelloponneseSELECT ?r ?rGeoWHERE{ ?r a ?rType ;noa:hasGeometry ?rGeo .FILTER(?rType = lgdo:Primary) .FILTER(strdf:contains("POLYGON((21.027 38.36, 23.77 38.36,23.77 36.05, 21.027 36.05,21.027 38.36))"^^strdf:WKT,?rGeo) ).}Retrieving a map layer (2/3)
  • 59. Get all capitals of prefectures of the Peloponnese.SELECT ?feature ?fName ?fGeoWHERE{?feature rdf:type gn:Feature;noa:hasGeography ?fGeo;gn:name ?fName;gn:featureCode ?fCode.FILTER(?fCode = gn:P.PPLA|| ?fCode = gn:P.PPLA2 ) .FILTER(strdf:contains("POLYGON((21.5136.41, 22.83 36.41, 22.8337.69, 21.51 37.69,21.51 6.41 ))”^^strdf:WKT, ?fGeo)).}Retrieving a Map Layer (3/3)
  • 60. Get all capitals of prefectures of the Peloponnese.SELECT ?feature ?fName ?fGeoWHERE{?feature rdf:type gn:Feature;noa:hasGeography ?fGeo;gn:name ?fName;gn:featureCode ?fCode.FILTER(?fCode = gn:P.PPLA|| ?fCode = gn:P.PPLA2 ) .FILTER(strdf:contains("POLYGON((21.5136.41, 22.83 36.41, 22.8337.69, 21.51 37.69,21.51 6.41 ))”^^strdf:WKT, ?fGeo)).}Retrieving a Map Layer (3/3)
  • 61. Final Map
  • 62. Semantic Enrichment for Hotspots Enrich hotspot products1. Connect each hotspot with amunicipality that it is located Improve accuracy with respectto underlying area2. Eliminate false alarms in sea3. Eliminate false alarms ininconsistent land cover areas4. Keep land part of the polygon
  • 63. Semantic Enrichment for Hotspots Enrich hotspot products1. Connect each hotspot with amunicipality that it is located Improve accuracy with respectto underlying area2. Eliminate false alarms in sea3. Eliminate false alarms ininconsistent land cover areas4. Keep land part of the polygon Improve accuracy with respectto temporal persistence of eachhotspots5. Remove “Christmas tree”effects”Christmas tree effect”:some hotspots appear in atimestamp, in the nexttimestamp they disappear,then they re-appear again,and so on.
  • 64. Correlate fire products with auxiliary data toincrease their thematic accuracy e.g., delete theparts of the polygons that fall into the sea.DELETE {?h noa:hasGeometry ?hGeo}INSERT {?h noa:hasGeometry ?dif}WHERE {SELECT DISTINCT ?h ?hGeo(strdf:intersection(?hGeo, strdf:union(?cGeo)) AS ?dif)WHERE {?h rdf:type noa:Hotspot.?h strdf:hasGeometry ?hGeo.?c rdf:type coast:Coastline.?c strdf:hasGeometry ?cGeo.FILTER( strdf:anyInteract(?hGeo, ?cGeo)}GROUP BY ?h ?hGeoHAVING strdf:overlap(?hGeo, strdf:union(?cGeo))}Improving the Accuracy of EO Data
  • 65. Correlate fire products with auxiliary data toincrease their thematic accuracy e.g., delete theparts of the polygons that fall into the sea.DELETE {?h noa:hasGeometry ?hGeo}INSERT {?h noa:hasGeometry ?dif}WHERE {SELECT DISTINCT ?h ?hGeo(strdf:intersection(?hGeo, strdf:union(?cGeo)) AS ?dif)WHERE {?h rdf:type noa:Hotspot.?h strdf:hasGeometry ?hGeo.?c rdf:type coast:Coastline.?c strdf:hasGeometry ?cGeo.FILTER( strdf:anyInteract(?hGeo, ?cGeo)}GROUP BY ?h ?hGeoHAVING strdf:overlap(?hGeo, strdf:union(?cGeo))}Improving the Accuracy of EO Data
  • 66.  The fire monitoring service was usedoperationally during the fire season of 2012 Used in a daily basis by the Greek civil protection agency Greek fire brigade Greek army Initial user feedback very encouraging!Fire monitoring service
  • 67.  Product ingestion, processing and refinement iscompleted in less than 12 seconds More refinement operations to be added later given thefive minutes time frameFire monitoring service
  • 68. ConclusionsIntroductionThe data modelstRDFThe query languagestSPARQLThe system StrabonExperimentalEvaluationReal Time FireMonitoringConclusions
  • 69. Future Work• Use even larger datasets• Compare with other systems• GeoSPARQL implementation of Oracle• stSPARQL query processing in MonetDB• Go distributed!• Federated queries
  • 70. Thanks! Any Questions?• Strabon• Manolis Koubarakis, Kostis Kyzirakos, Manos Karpathiotakis, Charalampos Nikolaou,Giorgos Garbis, Konstantina Bereta, Kallirroi Dogani, Stella Giannakopoulou andPanayiotis Smeros• Web site: http://strabon.di.uoa.gr• Mercurial repository: http://hg.strabon.di.uoa.gr• Trac: http://bug.strabon.di.uoa.gr• Mailing list: http://cgi.di.uoa.gr/~mailman/listinfo/strabon-users• Real Time Fire Monitoring Service,National Obervatory of Athens• http://papos.space.noa.gr/fend_static• Greek Linked Open Data• http://www.linkedopendata.gr• TELEIOS EU Project• http://www.earthobservatory.eu• SemsorGrid4Env EU Project• http://www.semsorgrid4env.eu

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