1. SemsorGrid4Env: Semantic Sensor Grids for Rapid Application Development forEnvironmental ManagementFP7-223913 EuropeanGeosciencesUnion 2010 FromSensorsto Interoperable Sensor Networks Vienna, 6th May 2010 Jean-Paul Calbimonte, Universidad Politécnica de Madrid semsorgrid4env.eu

3. Project Challenges and MainOutcomes

4. Project Plan & milestones

5. Highlights

6. Architecture

7. Data management

8. Registries

9. SemanticIntegration

10. ApplicationTierEGU 2010 - Vienna, 6 May 2010 2

11. TheTeam Universidad Politécnica de Madrid, (UPM, Spain) University of Manchester (UNIMAN, UK) National and KapodistrianUniversity of Athens (NKUA, Greece) University of Southampton (SOTON, UK) DeimosSpace SLU (DMS, Spain) EMU Ltd. (EMU, UK) TechIdeas (TI, Spain) 3 3 1 3 3 EGU 2010 - Vienna, 6 May 2010

13. Integrate with existing ones

15. Use data from multiple autonomous independently deployed sensor networks and other applications.4 EGU 2010 - Vienna, 6 May 2010

16. Main Outcomes (I) System Level (WP1) An architecturefor the design and implementation of open large-scale Semantic Sensor Grids. A reference SemsorGrid4Env implementation instantiating the architecture Component-level (WP2-WP5): New techniques and tools for semantic-based data management over the heterogeneous data streams that stem from autonomously deployed sensor networks. (WP2) Scalable and fault-tolerant resource discovery mechanisms for sensor registries. (WP3) The semantic infrastructure (including ontologies) needed to facilitate the integration of data coming from heterogeneous and distributed sensor networks, legacy databases and applications. (WP4) Higher-level application programming interfaces that ease the rapid generation of thin applications (e.g., mashups) of data from sensor networks and historical databases. (WP5) Two environmental management applications (WP6-WP7) 5 EGU 2010 - Vienna, 6 May 2010

17. Main Outcomes (II) Fire Risk Monitoring and Warning in Spain (technology-driven) Coastal and Estuarine Flood Warning in Southern UK. (established early adopter community) 6 EGU 2010 - Vienna, 6 May 2010

19. Significantpotentialfromemergingtechnologiestoassistusersby:

20. Improvedmonitoringbydeployed & emerging sensor networks

21. New capabilties in data integrationincludinglive data streams

22. Rapid development of flexible and user-centric decision support systems

23. Semantic Web supportingdiscovery and integration

24. SSG4Env combines expertise and technology in all of theseareastoprovidesolutionswhich are simple, live and dynamicEGU 2010 - Vienna, 6 May 2010

25. 8 WorkpackageStructure and Deliverables D1.1: Setup of software development technologies D1.2: Deployment of technologicalinfrastructure D1.3: SemsorGrid4Env Architecture D2.1: Data Requirements, Data Management and Analysis Issues and Query-Based Functionalities D3.1: Data models and languages for registries in SemsorGrid4Env D3.2: Distributed data structures and algorithms for a Semantic Sensor Grid registry D4.1: Design of the SemsorGrid4Env ontology-based data integration model D5.1: Specification of high-level application programming interfaces D6.1: Requirementsspecification D6.2: Deployment of the sensor network D7.1: Requirementsspecification D7.2: Deployment of the FloodNet sensor network in the Solent D8.1: Quality and Risk Contingency Plan D8.2: GenderAction Plan D9.1: SemsorGrid4Env Website D9.2: Plan forDisseminationActivities D9.3:SWOT Analysis 8 EGU 2010 - Vienna, 6 May 2010

26. Main Project Phases 9 EGU 2010 - Vienna, 6 May 2010

28. Pre-existing services may be called by any service.

29. Independent development of services.

31. Semantic middleware adds value to services in application and data tiers.10 EGU 2010 - Vienna, 6 May 2010

33. SNEE query processor / SNEEql query language

34. Documented requirements from use cases, to the level of queries and data analyses.

35. Support QoS-aware evaluation within in network query optimizer.

36. Developed out-of-network query compiler and evaluator from to support integration queries.SELECT RSTREAM t.id, w.speed, w.dirn FROM wind[NOW] w, tree[NOW] t WHERE t.smoke > 0 AND sqrt((t.locx - w.locx)^2 + (t.locy - w.locy)^2) <= 40 EGU 2010 - Vienna, 6 May 2010 11

38. Represent thematic and spatial metadata that change over time. Coupled with the RDFS/OWL ontologies of WP4.

39. Developed a formal semantics and algebra for stSPARQL on which we base our implementation.

40. Development of Strabon: a centralized implementation of a subset of stSPARQL.EGU 2010 - Vienna, 6 May 2010 12

42. Extend existing ontology-based data integration models to take into account sensor networks streaming data, semantic heterogeneity and quality of service

43. Specified a suite of sensor network ontologies that will be used for describing sensors and related data for the SemSorGrid4Env software architecture EGU 2010 - Vienna, 6 May 2010 13

44. WP2: Semantic Integrator Ontology-based data access O-O mapping S2O mappings Client Queryreconciliation q qr Query canonisation Qc SNEEql’ (S1 S2 Sn) SPARQLSTR (Og) SNEEql (S1 S2 Sn) SPARQLSTR (O1 O2On) DistributedQueryProcessing Data decanonisation Data reconciliation d dr Dc [tuplel1 l2 l3] [tripleO1 O2 On] [tripleOg] SemanticIntegrator EGU 2010 - Vienna, 6 May 2010 14

46. Supports applications in WP6 and WP7

47. Resource-centric including Linked Data

48. Embraces and investigates interplay of SOA and ROA15 EGU 2010 - Vienna, 6 May 2010

50. Sensor deployment in the UK Solent area

51. Early mashup developments for flood warning

52. In order to engage more quickly potential users and other stakeholders.16 EGU 2010 - Vienna, 6 May 2010

54. Architecture, validated with the application use cases (WP1)

55. Selection of outlier detection algorithms (WP2)

56. Out-of-network event stream query processor (WP2)

57. TinyOS2 code generator for the in-network SNEE (WP2)

58. Spatio-temporal extension of SPARQL (stSPARQL) (WP3)

59. Ontology-based streaming data access (WP4)

60. Selection of ontologies to be reused (WP4)

61. API combining RESTful and Linked Open Data approaches (WP5)

62. A proposal for the identification, naming and generation of Linked Stream Data (WP5)17 EGU 2010 - Vienna, 6 May 2010

63. SemsorGrid4Env: Semantic Sensor Grids for Rapid Application Development forEnvironmental ManagementFP7-223913 EuropeanGeosciencesUnion 2010 FromSensorsto Interoperable Sensor Networks Vienna, 6th May 2010 Jean-Paul Calbimonte EGU 2010 - Vienna, 6 May 2010

65. Combination of OGC – Semantic Web – REST technologies/approaches

66. ProposalforLinked Data about sensor data

67. Strongfocusonquery-basedaccessto data19 EGU 2010 - Vienna, 6 May 2010

69. Mashups require combined access to:

70. Sensed data from multiple sensors.

71. Stored data from multiple sources.

72. Ontologies for linking independent sources.

73. The aim of the architecture is to deliver appropriate abstraction and integration services for the mashups. EGU 2010 - Vienna, 6 May 2010

74. TheConsortiumClassified Sevenpartners Fouruniversities 2 SME 1 largecompany Fourmajorsectors Education IT AerospaceEngineering Environment Technologicalcorecompetencies Sensor Networks (UNIMAN, SOTON-ECS, NKUA) Semantics (UPM, UNIMAN, SOTON-ECS) Grid (UNIMAN, TI, SOTON-ECS, UPM) P2P (NKUA) Rapid ApplicationDevelopment (SOTON-ECS) Use Cases Floodwarning (EMU, SOTON-GEODATA) Firewarning (DMS) 21 EGU 2010 - Vienna, 6 May 2010

76. speed

77. direction

79. Transforming SPARQLSTR to SNEEql Semantic Integrator SELECTconcat(‘ssg4env.eu#WindSpeedMeasurement' , windsensor.id, windsensor.ts ) as a1 , concat( ‘ssg4env.eu#Sensor' , sensors.sensorid ) as a2 FROM sensors, windsensor[ FROM NOW - 10 TO NOW MIN] WHERE ( sensors.sensorid = windsensor.id ) PREFIX fire: http://www.semsorgrid4env.eu# PREFIX rdf: http://www.w3.org/1999/02/22-rdf-syntax-ns# SELECT ?speed ?name FROM STREAM <http://www.ssg4env.eu/Readings.srdf> [RANGE 10 MINUTE STEP 1 MINUTE] WHERE { ?WindSpeed a fire:WindSpeedMeasurement; fire:hasSpeed ?speed; fire:isProducedBy ?sensor; fire:hasTimestamp ?time. ?sensor a fire:Sensor; fire:hasName ?name. } Work in progress: removing redundant queries, basic optimisations, more complex scenarios