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Transcript
Knowledge Networks and Science Data
Ecosystems
December 7, 2012, AGU12 IN54A-02.
Peter Fox (RPI/ Tetherless World Constellation and WHOI/AOP&E)
[email protected]
What’s ahead/pre-summary
•  Data ecosystems necessarily involve a
variety of stakeholders
•  Complex relations require us to move
beyond ‘simple’ networks
•  To do that, we:
–  Define the framework for a knowledge
base, and populate it
–  Query and render the result
Tetherless World Constellation
2
Experience
•! Ecosystem
Data
Creation
Gathering
Information
Presentation
Organization
Knowledge
Integration
Conversation
Context
3
You mean, you want to
know?
What is a Knowledge Network? How does it work?
Rendering of a network
logd.tw.rpi.edu/demos
Marine ecosystems
Science ecosystem
<$'$#12(9-"&"1&"4$/
New National Ocean Policy =
Ecosystem Based Management
=>"'$'()*(5%"$-%$
Regional Academic Consortia =
PaCOOS + CINAR & CAMEO
BCO - DMO
!"#$%&$'()*(+,$-%*(.$$'/
NMFS / IEA
0-1)2$'()*(3#"41&$(5$%&6#(31#&-$#/7"8/(
Packard Foundation + NCEAS + PISCO
9-(5$#4"%$(:6(0-'(;/$#/
Policy Professionals, Fisheries Management Councils, NMFS & Others
(Center for Environmental Quality) CEQ + ESIP (Environmental Science Information Partners)
1$#/+.$+#&-+#(2.%+&7"3"8+,+3*&0&93*+8."*+4&'%(#)#*+,#&:##+##,+3*
Figure Acknowledgement: Suzanne Lawrence
7".$3+&;(3#+.<"*$(3&0&7"3"8+,+3*&=((5#&>&?%+3".$(#
'3<$.(3,+3*"5&@(5$%)&0&7"3"8+,+3*&@.(*(%(5#&A&93$*$"*$<+#
!"#$%&'%(#)#*+,#&-+#+".%/&0&1234",+3*"5&'%(#)#*+,#&6)3",$%#
Complex networks
‘Complex’ networks
•  Based on information content
–  Node count and number of links
–  Spanning height
–  ‘Width’
–  Etc.
•  Or complexity metrics, cf. McCabe –
number of linearly independent paths
through the network
•  Open world networks lead to differing
node relations… (or not)
•  Complex :== Meaning in context
•  Semantic networks are ones where the
nodes and relations are ‘named and
typed’
What about meaning
Named and typed relationships
Name1:type1
Namen:typen
Name2:type2
Name2:type2
Framework - DPSIR
forces and Pressures on the environment, the consequent State of the environment and its
Impacts, and the Responses undertaken, and of the interlinkages between each of these
elements. A generic DPSIR framework for water is shown in Figure 4.
Figure 4: A generic DPSIR framework for water
Responses
Driving
Forces
!
Water use restrictions
Alternative supplies
Subsidised water prices
Improved information
Demand side management
Voluntary agreements
Regional conflicts
Waste water treatment
Ban on products
Reservoirs
Industry
Energy
Agriculture
Aquaculture
Households
Tourism
Climate
Geology
Pressures
Climate change
Point source pollution
Diffuse source pollution
Water abstraction
Physical intrusions
Water quantity
Groundwater status
Ecological status:
chemical
physical
biological
State
Loss of habitats/species
Ill health
Droughts/floods
Desertification
Salinisation
Loss of amenity
Coastal erosion
Non-indigenous species
Eutrophication
Acidification
Impacts
The state of water is determined by natural factors such as geology and climate and also by
the pressures exerted by human activities. Many of the pressures and the underlying driving
forces are common to all or a number of the issues. For example, agriculture is a significant
storage reservoirs to safeguard supplies when other sources are stressed. Other measures are
aimed at reducing or controlling the demand for water including water pricing, water-saving
devices and reduction of water leakage in distribution systems.
Figure 5: DPSIR conceptual framework for assessing water quantity resources
Responses
Driving
Forces
!
Industry
Energy
Agriculture
Aquaculture
Households
Tourism
Climate
Overall reservoir stocks
Water prices
Water use efficiency
Water leakage
Pressures
Climate change
Total abstractions
Sectoral water use:
agriculture, industry
households, tourism
Available water
State
3/9/12
Freshwater shortage,
Modification of streamflows
Saltwater instrusion
Groundwater levels
Impacts
Water Quantity
14
Organic pollution and eutrophication
The effects on the aquatic environment of organic pollution, caused by discharges from waste
water treatment plants, industrial effluents and agricultural run-off, include reduced river
Models for ecosystems
Instances
Network Scale(s)
•  Complex :== Meaning
•  Semantic networks are ones where the
nodes and relations are ‘named and
typed’
•  Interesting property - scale-free
–  Citation networks
–  The Web
–  Semantic networks
–  Depend on super nodes
Scale free?
More likely – multi-scale with some hierarchy …
Vision being implemented
•  “Our vision is to develop, facilitate, and maintain
sustained multi-way engagement of natural and
social scientists and many practitioners in multiscale local to global networks for Large Marine
Ecosystems (LMEs)”.
•  Goal: Perform routine assessments of LMEs
involving all (or as many) stakeholders and we
want robust science data presented in forms that
various end-users can consume…
Discussion
•  We have the tools to explore these
networks, collaboratively…
•  Now looking at network rendering, i.e.
queries and visualizations
•  Current limit is: base information models
upon which to develop the initial
knowledge base(s) (i.e. ontologies)
•  Next: Compute deductive closure,
iterate… ?
•  Thanks for listening. Questions?