scholarly journals Implementing the formal language of the vegetation classification expert systems (ESy) in the statistical computing environment R

2021 ◽  
Vol 24 (1) ◽  
Author(s):  
Helge Bruelheide ◽  
Lubomír Tichý ◽  
Milan Chytrý ◽  
Florian Jansen
Keyword(s):  
Expert Systems ◽  
Formal Language ◽  
Vegetation Classification ◽  
Statistical Computing ◽  
Computing Environment

XploRe: An Interactive Statistical Computing Environment.

1996 ◽  
Vol 45 (3) ◽  
pp. 388
Author(s):  
Robert Bartels ◽  
W. Hardle ◽  
S. Klinke ◽  
B. A. Turlach
Keyword(s):  
Statistical Computing ◽  
Computing Environment

scholarly journals Shp2graph: Tools to Convert a Spatial Network into an Igraph Graph in R

2018 ◽  
Vol 7 (8) ◽  
pp. 293 ◽  
Author(s):  
Binbin Lu ◽  
Huabo Sun ◽  
Paul Harris ◽  
Miaozhong Xu ◽  
Martin Charlton
Keyword(s):  
Network Analysis ◽  
Road Network ◽  
R Package ◽  
Statistical Computing ◽  
Spatial Network ◽  
Computing Environment ◽  
Vast Array ◽  
Urban Road ◽  
Analytical Tools ◽  
Statistical Functions

In this study, we introduce the R package shp2graph, which provides tools to convert a spatial network into an ‘igraph’ graph of the igraphR package. This conversion greatly empowers a spatial network study, as the vast array of graph analytical tools provided in igraph are then readily available to the network analysis, together with the inherent advantages of being within the R statistical computing environment and its vast array of statistical functions. Through three urban road network case studies, the calculation of road network distances with shp2graph and with igraph is demonstrated through four key stages: (i) confirming the connectivity of a spatial network; (ii) integrating points/locations with a network; (iii) converting a network into a graph; and (iv) calculating network distances (and travel times). Throughout, the required R commands are given to provide a useful tutorial on the use of shp2graph.

INTEGRATING DISTRIBUTED EXPERTISE

1992 ◽  
Vol 01 (03n04) ◽  
pp. 393-410 ◽  
Author(s):  
EVANGELOS SIMOUDIS ◽  
MARK ADLER
Keyword(s):  
Problem Solving ◽  
Expert Systems ◽  
Real World ◽  
Computational Models ◽  
Knowledge Based Systems ◽  
Computing Environment ◽  
The Past ◽  
Knowledge Based ◽  
Narrow Scope ◽  
Distributed Expertise

Over the past ten years a myriad of knowledge-based expert systems have been developed and deployed. These systems have a narrow scope and usually operate in stand-alone mode. They also follow different implementation philosophies and use a variety of reasoning methods. To address problems of wider scope, researchers have developed systems that utilize either centralized or distributed computational models. Each of these systems is homogeneous, and due to the way developed, prohibitively expensive for real-world settings. In this paper we present OMNI, a framework for integrating existing knowledge-based systems in a way that they can cooperate during problem-solving while they remain distributed over a computing environment.

A New Generation of a Statistical Computing Environment on the Net

COMPSTAT ◽  
1996 ◽  
pp. 135-148
Author(s):  
Swetlana Schmelzer ◽  
Thomas Kötter ◽  
Sigbert Klinke ◽  
Wolfgang Härdle
Keyword(s):  
Statistical Computing ◽  
Computing Environment ◽  
New Generation
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