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  • From: "Lawrence F. London, Jr." <lfl@intrex.net>
  • To: Permaculture Plant Database <pcplantdb@lists.ibiblio.org>
  • Subject: Re: [pcplantdb] Synth, comments, etc || EIE
  • Date: Mon, 13 Dec 2004 00:06:14 -0800

Stephanie Gerson wrote:

...What we'd be trying to do is to collapse a _network_ down as if it was a hierarchy. It means you'd probably need a way to discover the subnetworks within the network, and lump the subnetworks together.

AND THIS IS WHERE I THINK ECOSYSTEM INFORMATION ENGINEERING WOULD BE USEFUL. OK, MAYBE I'LL GET ON THAT TODAY...

[On Elastic Interval Geometry:] I think TouchGraph uses it. It's a front-
end thing. Imagine a network of nodes. Nodes are connected to other nodes, and each connection has an optimal distance. The connections behave like springs, pushing the nodes back and forth on the graph, until all the nodes finally self-assemble into an optimal arrangement. It's fun to watch actually. I'm developing something right now, though it's in RealBasic (Mac only), though a friend will be porting it over to C. I may be able to develop a front-end for this back-end, given enough relationship info.

KEL
Knowledge Engineering Laboratory
http://kelab.tamu.edu/standard/approach.html
Knowledge Engineering For Ecosystem Management
Ecosystem management deals with subjects that typically have large and disparate knowledge bases. The data and information that form the knowledge base for a specific problem often come from several different domain specialties, e.g., ecology, geography, sociology, economics. The knowledge base can exist in several forms: (i) tabular information [usually stored in a database management system], (ii) spatially referenced data themes [usually associated with a geographic information system - GIS], (iii) numerical output from simulation models and mathematical evaluation functions, and (iv) heuristics of experts [based on corporate experiences of humans]. Although the knowledge base for most problems in ecosystem management is substantial, it is also incomplete and in a state of evolution. Knowledge engineering is an activity that embraces a set of concepts and methodologies dealing with (i) acquisition of knowledge, (ii) analysis and synthesis of data and information [quantities], (iii) integration and interpretation of knowledge [quantities and qualities], and (iv) application of knowledge (Figure). The goal of this activity, in the context of ecosystem management, is to facilitate use of the full extent of knowledge available for the purpose of solving a problem, supporting decisionmaking, or developing a plan of action. Historically, scientists have also used personal flavors of this basic approach to conduct, summarize, and report their research discoveries. Computer-based tools and technologies have been created to formalize and automate the process, thus greatly expanding human capabilities. Knowledge engineering can be viewed as a computational approach to ecosystem management. It is a direct consequence of the development of digital computers and the infusion of this technology into society. Each of the elements of knowledge engineering is briefly described below. The elements include acquisition of knowledge, analysis and synthesis of data and information, and integration and interpretation of knowledge.

The Journal of Industrial Ecology - Industrial Ecology and Ecological
Engineering - The MIT Press
http://mitpress.mit.edu/catalog/item/default.asp?sid=C4A58A00-B1CC-41E4-853B-B3FD5254972E&ttype=4&tid=32


--
L.F.London
lfl@intrex.net
http://market-farming.com
http://www.ibiblio.org/ecolandtech




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