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Semelhante a Icm sem tech_master
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- 1. Case study Linked-data forLinked-data for Integrated CatchmentIntegrated Catchment ManagementManagement Ian Dickinson Epimorphics Ltd ian@epimorphics.com @ephemerian Tom Guilbert Environment Agency tom.guilbert@ environment-agency.gov.uk
- 2. Agenda context and aims – catchment management data – vision integrated catchment linked-data project conclusion
- 3. A better place for people and wildlife
- 5. Data overview water bodies risk assessments classification results reasons for failure predicted outcomes actions
- 7. 1. Data & evidence, consultations, local knowledge, model outputs and plans collated in to a shared central system “Local Community CPS” Local Community Catchment Planning System Local Community Catchment Planning System MonitoringMonitoring Local Knowledge Local Knowledge ActionsActions 2. Contents of Local Community CPS published as Linked Data alongside EA and research datasets 3. Linked Data (machine readable data) could be automatically combined by applications such as the EVO, CCM Hub and any number of web apps CCM HUB Slide used by kind permission of Michelle Walker, Rivers Trust
- 9. ICM proof-of-concept project 16 weeks duration project team: – 1 FTE app dev – 0.4 FTE user research – 0.5 FTE data 7400 water bodies 7.8m triples agile principles – four iterations – 2-3 week sprints – stakeholder review alpha/staging site organizationscale
- 10. From data to linked open data data modelling extraction transformation publication presentation interpretationdownload source data SQL Java Apache Fuseki explorer application Elda
- 11. Modelling: considerations every constant becomes a URI plan for change re-use vocabularies complete is better than simple
- 12. Data complexity WaterBody SurfaceWater GroundWater RiverOrLake Transitional Coastal River Lake SurfaceWaterTransfer Canal SSSI_Ditch
- 13. Data complexity WaterBody SurfaceWater GroundWater RiverOrLake Transitional Coastal River Lake SurfaceWaterTransfer Canal SSSI_Ditch
- 14. Data transformation in: CSV out: RDF triples iterative, so automate!
- 15. Data publishing Baseline goal: – provide access to the data Practical considerations: – Just “follow-your-nose” linked data? – or SPARQL? – or an API? – ….
- 17. Published data: linked-data API
- 18. Published data: linked-data API
- 19. Published data: linked-data API
- 20.
- 21. ICM data explorer present the data in a meaningful way provide meaningful and useful interactions
- 22. Data explorer key features search – by name, catchment, location, ... show classification items filter by properties – e.g. classification value map and tabular output basic reports download data
- 23. Data explorer application Specific understanding of user goals and task Generic data-driven interface data explorer
- 25. Data explorer application Easy for novices to get started Not too frustrating and slow for experienced users data explorer
- 26. Typical user enquiry “Please show me all: – rivers and lakes – near Glastonbury – that had overall ecological classification as moderate, poor or bad – between 2009 and 2012.”
- 30. Demo
- 31. Initial learnings writing SPARQL by doing – in context – with feedback hard to balance different user needs – explore vs. guide – real user input download – important – RDF to useful CSV is hard
- 32. Dissemination
- 33. Conclusions & next steps formal evaluation – involve partner organizations eg Rivers Trust “generated excitement” – key engagement tool for catchment management information – summer 2014 draft river basin management plans big picture – reference spine for integrating data from other environmental stakeholders
- 34. Photo courtesy of grisleyreg http://www.panoramio.com/photo/65014213 License CC BY-NA 3.0 Questions?