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SSG4Env WP4

SSG4Env WP4. Semantic Integrator Proposal & WP2 Collaboration. WP4. Design & implement Semantic Integrator Service D4.1 Related work on Semantic data integration, Ontology-based data access Semantic queries over streaming data

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SSG4Env WP4

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  1. SSG4Env WP4 SemanticIntegratorProposal & WP2 Collaboration

  2. WP4 • Design & implementSemantic Integrator Service • D4.1 • Related work on Semantic data integration, Ontology-based data access • Semantic queries over streaming data • Proposal of Ontology-base integration model for heterogeneous streaming and stored data

  3. Requirements Sensor Network Database Data Decl. Query Integrate Integrated view Stream Data • Based on use-cases • Integrate stored and streaming data sources • Integrate sources through unified view • Pose declarative queries over integrated view

  4. Background • Ontologybased data access • Ontology-based data integration • Streaming data access • Distributedqueryprocessing • SNEE/SNEEql • R2O/ODEMapster • SPARQL streamingextensions

  5. Ontology-based data access & integration SquirrelRDF RDBToOnto Relational.OWL SPASQL Virtuoso D2RQ MASTRO R2O + ODEMapster ODEMapster MySQL Oracle ...others ODEMQL Ontological query Transform relational query Ontological model Database OWL Mapping R2O OBSERVER SIMS Carnot DWQ PICSEL MOMIS Ontological query Ontological model Transform relational query Databases Mappings

  6. Streaming Data Access • Continuously appended data • Potentially infinite • Time-stamped tuples • Continuous queries • Latest information used in queries • Windows: time and tuple based • Archival data (t9, a1, a2, ... , an) (t8, a1, a2, ... , an) (t7, a1, a2, ... , an) ... ... (t1, a1, a2, ... , an) ... ... Window [t7 - t9] Streaming Data • Sensor Networks characteristics • Low computational, power resources, storage • Distributed query execution • Routing, Optimization SNEE Query SNEEql

  7. SPARQL streaming extensions • SPARQL RDF query language • Language limitations for streams • Windows, time, tuple • Data model • Aggregates, stream operators • Streaming SPARQL • C-SPARQL

  8. Approach • Establish global ontological view • Mapping global to local views • Mapping ontological model to stream/stored sources • Define semantic streaming queries over ontological model • Transform semantic queries to inter-lingua streaming query language • Distributed query evaluation in distributed sources • Integration and transformation of results

  9. SemanticIntegrator Ontology-to-Ontologymappings S2O mappings Client q Query reconciliation qr Query canonisation Qc SNEEql (S1 S2 Sn) SPARQLSTR (Og) SPARQLSTR (Og) Query Processing Dl Data reconciliation Data decanonisation [tuplel1 l2 l3] [tuple] d dr [tripleOg] [tripleO1O2On] Ontology-based Streaming Data Access Service

  10. SemanticIntegrator S2O mappings q Qc Client Query canonisation SNEEql (S1 S2 Sn) SPARQLSTR (Og) Query Processing Data decanonisation d Dl [tuple] [tuplel1 l2 l3] [tripleOg] Ontology-based Streaming Data Access Service

  11. DistributedQueryProcessing AbstractSyntaxTree LogicalAlgebraicForm PhysicalAlgebraicForm DistributedAlgebraicForm Logical rewrite Physicaloptimization Partition Leafspecific scans Parsequerystring, usinggrammar, produce AST Produce logical plan Produce physical plan includingoptimizations Identifyexchangepoints, addtothedistribute plan Retrieve data fromexternalsources Parse

  12. Architecture

  13. Integration Interface • IntegrateAsoperation • Reference to sources • Mapping Global-to-Local ontologies • Sources have an ontologicalviewregistered • Mappingsfromontologicalviews to inter-lingua • Returnsreference to integrated data resource

  14. Query Interface • ExecuteQuery • One shotqueries • ExecuteQueryFactory • Used for most streaming queries • Pull/push in config document • Push delivery -> use Subscription • Pull delivery -> use Data Access • TypicallyStreamExecuteFactory

  15. Cross-cutting issues • Identifyqueriesfromboth use-cases • Characterization of queries • Representqueries in global querylanguage • Study the querysemantics of QL for RDF Streamscompared to SNEEqlsemantics

  16. R2O + ODEMapster • Startingwith SPARQL support • Define «S2O » extensions for R2O • Define SPARQLSTRlanguagesyntax and semantics • Engine support for « S2O » documents, SPARQLSTR queries • Engine support for SNEEql translation and connection

  17. Thanks... windsamples

  18. conceptmap-def WindSpeedMeasurement virtualStream<http://ssg4env.eu/Readings.srdf> uri-asconcat('ssg4env:WindSM_', windsamples.sensorid,windsamples.ts) attributemap-defhasSpeed operationconstant has-columnwindsamples.speed dbrelationmap-defisProducedBy toConceptSensor joins-viacondition equals has-columnsensors.sensorid has-column windsamples.sensorid conceptmap-def Sensor uri-asconcat('ssg4env:Sensor_',sensors.sensorid) attributemap-defhasSensorid operation constant has-columnsensors.sensorid WindSpeed Measurement s:windsamples hasSpeed sensorid: int PK ts: datetime PK speed: float isProducedBy xsd:float Sensor t:sensors hasSensorid sensorid: int PK sensorname: st xsd:int Streams S2O Mapping Ontologies

  19. Extras

  20. Thrutheexample… Consider a simple query: obtainthecurrentmeasuredwindspeed and direction, measurement time and sensor location. Speed and directionretrievedfrom sensor network. Sensor locationfoundon a table.

  21. SPARQLSTRQuery REGISTER … PREFIX fire <http://.../integratedOntology> SELECT ?long ?lat ?speed ?dir ?ts FROM STREAM <integratedOntologicalView> [NOW] WHERE { ?id a fire:WindSensor; fire:hasLong ?long; fire:hasLat ?lat; fire:hasSpeed ?speed fire:hasDir ?dir fire:hasTS ?ts. } Notrestrictedto C-SPARQL orStreaming SPARQL limitations (no reason) Join of twodistributedsources Join of stream and storedsources

  22. SPARQLSTRon Local Ontologies REGISTER … PREFIX wind <http://.../WindOntology> PREFIX loc <http://.../LocationOntology> SELECT ?long ?lat ?speed ?dir ?ts FROM STREAM <WindSensor> [NOW] WHERE { ?wind a wind:Sensor; wind:hasSpeed ?speed; wind:hasDirection ?dir; wind:hasTS ?ts. ?locid a loc:Location; loc:hasLong ?long; loc:hasLat ?lat. }

  23. SNEEql query RSTREAM SELECT p.long, p.lat, w.speed, w.dir, w.ts FROM Wind [NOW] as w, Placement [NOW] as p WHERE p.id=w.id

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