Topic's layer distribution
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| − | + | Topic's layer distribution is an ordered set of numbers representing topic's distribution between layers. Layer distribution is used to give a rapid outlook of the topic and how the topic is merged between layers. | |
| − | + | Topic's layer distribution is shown at the footer, near bottom right corner of the Wandora window. Layer distribution contains one number for each layer. First number represents the first layer, second number the second layer etc. Numbers build up a vector, or a tree structure if layer stacks are nested. | |
| − | + | If the number in the layer distribution is 0 (zero), current topic doesn't exist in the layer. If the number is 1 (one), current topic has one merged topic in the layer. If the number is 2, topic has two merged topics in the layer etc. A number in layer distribution vector is always positive integer number or zero. | |
| − | + | For example, if we have a topic with layer distribution '''[ 1:1:0 ]''', we can conclude the topic is a merged one and contains a topic in the first and the second layer but not in the third layer. Notice, the layer distribution vector doesn't say any reason for merge. Topics in Wandora merge when they share a base name, a subject locator or a subject identifier. | |
| − | Some | + | If we have a layer distribution '''[ 1:2 ]''', we can conclude the topic is a merged one but also that the topic in first layer causes two different topics to merge in second layer. This is possible when a topic in the first layer shares a base name and a subject identifier with two distinct topics in layer 2, for example. |
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| + | Some distributions are impossible. For example, situation where a single layer contains two or more merged topics can never occur as the virtual merge collapses into a real one as soon as topics share identifier. '''[ 2:0:0 ]''' is an example of such '''impossible''' layer distribution. | ||
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| + | If layer structure contains nested layer stacks (i.e. layer groups within layers) layer distribution is a tree structure. For example '''[ 1:[ 0:1 ] ]''' is such a layer distribution where Wandora contains a layer stack with one topic map layer and one nested layer stack. Nested layer stack contains two layers. Inspected topic is a merged topic with two origins, one from first topic map and one from the second layer in nested layer stack. | ||
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| + | == Math with layer distribution? == | ||
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| + | This is very interesting question. Can one calculate anything reasonable with the layer distribution? What rules apply to the layer distribution? What kind of mathematical object it is? We are open for your suggestions and welcome all ideas. | ||
Latest revision as of 18:37, 6 May 2012
Topic's layer distribution is an ordered set of numbers representing topic's distribution between layers. Layer distribution is used to give a rapid outlook of the topic and how the topic is merged between layers.
Topic's layer distribution is shown at the footer, near bottom right corner of the Wandora window. Layer distribution contains one number for each layer. First number represents the first layer, second number the second layer etc. Numbers build up a vector, or a tree structure if layer stacks are nested.
If the number in the layer distribution is 0 (zero), current topic doesn't exist in the layer. If the number is 1 (one), current topic has one merged topic in the layer. If the number is 2, topic has two merged topics in the layer etc. A number in layer distribution vector is always positive integer number or zero.
For example, if we have a topic with layer distribution [ 1:1:0 ], we can conclude the topic is a merged one and contains a topic in the first and the second layer but not in the third layer. Notice, the layer distribution vector doesn't say any reason for merge. Topics in Wandora merge when they share a base name, a subject locator or a subject identifier.
If we have a layer distribution [ 1:2 ], we can conclude the topic is a merged one but also that the topic in first layer causes two different topics to merge in second layer. This is possible when a topic in the first layer shares a base name and a subject identifier with two distinct topics in layer 2, for example.
Some distributions are impossible. For example, situation where a single layer contains two or more merged topics can never occur as the virtual merge collapses into a real one as soon as topics share identifier. [ 2:0:0 ] is an example of such impossible layer distribution.
If layer structure contains nested layer stacks (i.e. layer groups within layers) layer distribution is a tree structure. For example [ 1:[ 0:1 ] ] is such a layer distribution where Wandora contains a layer stack with one topic map layer and one nested layer stack. Nested layer stack contains two layers. Inspected topic is a merged topic with two origins, one from first topic map and one from the second layer in nested layer stack.
[edit] Math with layer distribution?
This is very interesting question. Can one calculate anything reasonable with the layer distribution? What rules apply to the layer distribution? What kind of mathematical object it is? We are open for your suggestions and welcome all ideas.
