Icm sem tech_master

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  • 1. Case studyLinked-data forLinked-data forIntegrated CatchmentIntegrated CatchmentManagementManagementIan DickinsonEpimorphics Ltdian@epimorphics.com@ephemerianTom GuilbertEnvironment Agencytom.guilbert@environment-agency.gov.uk
  • 2. Agendacontext and aims catchment management data visionintegrated catchment linked-data projectconclusion
  • 3. A better place for people and wildlife
  • 4. Catchment management
  • 5. Data overviewwater bodiesrisk assessmentsclassification resultsreasons for failurepredicted outcomesactions
  • 6. 1. Data & evidence,consultations, localknowledge, model outputsand plans collated in to ashared central system LocalCommunity CPSLocal Community CatchmentPlanning SystemLocal Community CatchmentPlanning SystemMonitoringMonitoring LocalKnowledgeLocalKnowledge ActionsActions2. Contents of LocalCommunity CPSpublished as LinkedData alongside EAand researchdatasets3. Linked Data (machinereadable data) could beautomatically combined byapplications such as the EVO,CCM Hub and any number ofweb appsCCMHUBSlide used by kind permission of Michelle Walker, Rivers Trust
  • 7. ICM: proof-of-concept project
  • 8. ICM proof-of-concept project16 weeks durationproject team: 1 FTE app dev 0.4 FTE userresearch 0.5 FTE data7400 water bodies7.8m triplesagile principles four iterations 2-3 week sprints stakeholderreviewalpha/staging siteorganizationscale
  • 9. From data to linked open datadata modellingextractiontransformationpublicationpresentationinterpretationdownloadsource dataSQLJavaApache Fusekiexplorer applicationElda
  • 10. Modelling: considerationsevery constant becomes a URIplan for changere-use vocabulariescomplete is better than simple
  • 11. Data complexityWaterBodySurfaceWater GroundWaterRiverOrLake Transitional CoastalRiver LakeSurfaceWaterTransfer Canal SSSI_Ditch
  • 12. Data complexityWaterBodySurfaceWater GroundWaterRiverOrLake Transitional CoastalRiver LakeSurfaceWaterTransfer Canal SSSI_Ditch
  • 13. Data transformationin: CSVout: RDF triplesiterative, so automate!
  • 14. Data publishingBaseline goal: provide access to the dataPractical considerations: Just follow-your-nose linked data? or SPARQL? or an API? .
  • 15. Published data: SPARQL?
  • 16. Published data: linked-data API
  • 17. Published data: linked-data API
  • 18. Published data: linked-data API
  • 19.
  • 20. ICM data explorerpresent the data in a meaningful wayprovide meaningful and useful interactions
  • 21. Data explorer key featuressearch by name, catchment, location, ...show classification itemsfilter by properties e.g. classification valuemap and tabular outputbasic reportsdownload data
  • 22. Data explorer applicationSpecificunderstandingof user goalsand taskGenericdata-driveninterfacedata explorer
  • 23. Data explorer applicationInterpretationandreportingExtractanddownloaddata explorer
  • 24. Data explorer applicationEasy fornovicesto getstartedNot toofrustratingand slowfor experiencedusersdata explorer
  • 25. Typical user enquiryPlease show me all: rivers and lakes near Glastonbury that had overall ecological classification asmoderate, poor or bad between 2009 and 2012.
  • 26. Dialogue movescorrespondingSPARQLqueryselectedRDFresourcescorrespondingSPARQLqueryselectedRDFresourcesinteractionstatelocation,classifications,water-body types,...interactionstateadd year constraint
  • 27. Demo
  • 28. Initial learningswriting SPARQL by doing in context with feedbackhard to balance different user needs explore vs. guide real user inputdownload important RDF to useful CSV is hard
  • 29. Dissemination
  • 30. Conclusions & next stepsformal evaluation involve partner organizations eg Rivers Trustgenerated excitement key engagement tool for catchment managementinformation summer 2014 draft river basin management plansbig picture reference spine for integrating data from otherenvironmental stakeholders
  • 31. Photo courtesy of grisleyreg http://www.panoramio.com/photo/65014213 License CC BY-NA 3.0Questions?