ICM Setup and Installation Guide for Cisco Unified ICM/Contact ...
Icm sem tech_master
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Transcript of Icm sem tech_master
Case study
Linked-data forLinked-data forIntegrated Catchment Integrated Catchment
ManagementManagement
Ian DickinsonEpimorphics Ltd
@ephemerian
Tom GuilbertEnvironment Agency
Agenda
context and aims– catchment management data– vision
integrated catchment linked-data project conclusion
A better place for people and wildlife
Catchment management
Data overview
water bodiesrisk assessments
classification resultsreasons for failure
predicted outcomesactions
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
CCMHUB
Slide used by kind permission of Michelle Walker, Rivers Trust
ICM: proof-of-concept project
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
From data to linked open data
data modelling
extraction
transformation
publication
presentation
interpretationdownload
source data
SQL
Java
Apache Fuseki
explorer application
Elda
Modelling: considerations
every constant becomes a URI
plan for change
re-use vocabularies
complete is better than simple
Data complexity
WaterBody
SurfaceWater GroundWater
RiverOrLake Transitional Coastal
River Lake
SurfaceWaterTransfer Canal SSSI_Ditch
Data complexity
WaterBody
SurfaceWater GroundWater
RiverOrLake Transitional Coastal
River Lake
SurfaceWaterTransfer Canal SSSI_Ditch
Data transformation
in: CSV out: RDF triples
iterative, so automate!
Data publishing
Baseline goal:– provide access to the data
Practical considerations:– Just “follow-your-nose” linked data?– or SPARQL?– or an API?– ….
Published data: SPARQL
?
Published data: linked-data API
Published data: linked-data API
Published data: linked-data API
ICM data explorer
present the data in a meaningful way
provide meaningful and useful interactions
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
Data explorer application
Specificunderstandingof user goalsand task
Genericdata-driven
interface
data explorer
Data explorer application
Interpretationandreporting
Extractand
download
data explorer
Data explorer application
Easy fornovicesto get started
Not toofrustrating
and slowfor experienced
users
data explorer
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.”
Dialogue moves
correspondingSPARQLquery
selectedRDF
resources
correspondingSPARQLquery
selectedRDF
resources
interactionstate
location,classifications,water-body types,...
interactionstate
add year constraint
Demo
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
Dissemination
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
Photo courtesy of grisleyreg http://www.panoramio.com/photo/65014213 License CC BY-NA 3.0
Questions?