The self-organizing maps used are composed of an input layer of a number of units equal to the number of resources. The output is made over a grid lattice with 64 by 64 units.
The training of the self-organizing maps was the most computational intensive task. For the large datasets, it takes more than 24 hours of computation to complete. Hopefully, a parallel implementation of the learning algorithm is possible (see section 5.7 "Parallel implementation" on page 34).
Having completed the self-organizing maps training process, each resource was presented to the network, which responded with a winning neuron, the location of the resources on the map.
To allow interaction with these maps, they have been made available on-line on the World-Wide Web. Using HTML forms [Berner-Lee et al., 1995b] generated by programs that comply with the CGI (Common Gateway Interface) [Robinson, 1995] specifications [Grobe, 1995], a graphical user interface has been created. Therefore, the graphical user interface is made using dynamic hypermedia documents. Figure 14 depicts its visual aspect.