05 November 2010

Knowledge Maps – Part VI: Patterns

    A pattern could be considered as a perceptual structure derived from observed similarities existing in the structures existing in a given layer. Following the DIKW pyramid, from data to information, knowledge and further to wisdom and beyond, could be observed various patterns that offer new insights at the respective levels, patterns that could categorize the findings in layers beyond the layers in which they are observed. We could discuss thus about data patterns referring to patterns existing in data, typically aggregated in form of information, information patterns referring to patterns existing in knowledge, typically aggregated in form of knowledge, knowledge patterns referring to patterns existing in knowledge, typically aggregated in form of wisdom and eventually wisdom patterns, referring to patterns existing in wisdom.

   The K-map, as a graphical tool used to represent chunks of information/knowledge and the associations existing between them, is not only a container for patterns, but a pattern itself, and this because the various types of K-maps are attempting to address special patterns in knowledge and its representational layout: chained, clustered, hierarchical, radial or networked. Patterns could be observed in the smallest representational elements existing in K-maps, the way the chunks, called nodes, are associated together forming micro-patterns, and further aggregated in creating macro-patterns. Ignoring the content of each node, and the meaning of associations, but keeping for example the direction of the associations, could be observed several simple patterns:

 KM - Patterns 1

      The above patterns are not necessarily representative, their scope being to provide an idea of what a pattern looks like: sequence (i), parallel sequences (ii), triangle (iii) & (iv), square (v), divergent (vi), convergent (vii) and any combination of them. For example the simplest pattern, the sequence, could be observed in all the other patterns excepting (xii) and (xiii) patterns, and it could include any numbers of nodes. Maybe more difficult to observe, the patterns (vi) and (vii) could be found in (viii) and (ix), in fact the number of emerging or emerging arrows from node could be greater than two. The number of such patterns could be in theory infinite, the nature offering a huge collection of such patterns, while many of the above patterns could be observed in K-maps too, the arrow playing the role of named or unnamed associations. Here are some variations of the above patterns based on concepts and the associations between them:

KM - Patterns 2

     The concepts (“Concept i”, i=1, 2, 3, 4) from the above diagram stand as placeholders for any concepts that could fit in such patterns. For example “data”, “information”, “knowledge” and “wisdom” are forming a sequence based on same association which could be named “leads to”, “forms”, “aggregated in”, etc. Causality relations could be modeled with such sequences, for example “high data quality” leads to “accurate report”, which leads to “accurate analysis”, which leads to “better decisions”. However causality is more complex, witness being the various Causal Maps available on the Web, for example “theory”, “concept” and “grounding”, respectively “customer satisfaction”, “product quality”, “customer loyalty” and “brand strength” seems to “break” the sequence pattern. Please note that the associations don’t necessarily to be true, they could represent as well an opinion or be rooted in experiments that confirms them. The laws of causality, complexity and patterns are too complex to be debated in here, in fact each person accumulated in time knowledge that matches such patterns.
   KM - Patterns 3

     In the above diagram, could be observed that the examples based on causality are situated at the top, respectively at the bottom of the diagram, in between different types of associations. Is it there any reasons for that? In the moment we asked ourselves such a question, we are already attempting to identify a spatial pattern referring to the positioning in a given area, in this case a diagram. Spatial patterns are at their turn quite complex patterns, we could see it in the world around us. Does the fact that the same distance exists between various points, that forms are contained one in the other or that the associations are dispersed radially from a common point tell us something? What do such patterns represent for us? Sometimes we have to make abstraction of the associations and content and see the macro-forms created by clusters of such patterns in order to discern the patterns. Does it seem too complicated? It isn’t at all. Just look at a poem formed of 4 strophes and a common rhyme. We are ignoring in fact what the words represent, the letters of a word, or the points that form a letter, we consider just the form of the strophes and the endings, that’s such a pattern. In nature there are so many such patterns, some even more simple than we might think of, and here are some examples:
 KM - Patterns 4
       The world of patterns doesn’t stop here, we could associate patterns related to all our senses, resulting thus olfactory, tactile, auditory or kinesthetic patterns. It seems that such patterns are moving beyond our representational sphere, doesn’t it? Not at all, everything could be reduced to a concept and the associations between them, the difficulty residing in how to choose the “best” pattern that reveals the represented knowledge.

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