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I. THEORY – 1. Epistemic system(s)

Cognitive impact of databases

Giorgio Buccellati – January 2022

The cognitive impact – 1

searching for the known
unknown is the already known, means that we know the known
sense of control over the known
relatinship of cognition (how to know) to epistemics (what to know)

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The cognitive impact – 2

There is a major epistemic implication to all this. Even though in either type of sorting the sequence is not “read” from top to bottom, the top and bottom are known to be there and can easily be reached, as with the two covers of a book. The cognitive impact is highly significant: one gains a new sense of the organic connections of the data, similar to what one has with the sequential reading of a book, where the flow of the argument provides the connecting broad link between the beginning and the end. In an unsorted database there is no conceptual beginning and end: the beginning is simply the start of the data entry process, and the end is the finishing moment of the same process, without regard for the substance of the content. When sorted, on the other end, the beginning and end are the result of an explicit choice and the data are rearranged to fit them.

It is a dynamic view because data can be re-arranged at will, choosing different columns at different times as the primary criterion. This is one of the great advantages of digital databases: it indicates that boundaries are not only perceivable, but can be re-adjusted at will, causing thereby a complete reconfiguration of the data ‑ as if reprinting a book several times, each time with a different pagination.

The various graphic renderings that are common to all commercial programs (such as Excel) help to emphasize this point. Bar histograms or pie charts bring out visually the boundaries of the whole in which the details are enclosed: a graphic rendering configures sorts in ways that starkly highlight the outer boundaries of the sorted data.

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The urge towards the whole

What emerges from these considerations is a new way in which we can achieve an explicit perception of the whole. It is a very different perception from the fixed dimension of a printed format, one that has gradually come into view escaping notice. It is as if a renewed affirmation of the need to relate to the entirety of the data and not just to the particular instances, to the whole and not just to the fragments.

The question of “big data” can be seen in this light. There is practically no limit to the amount of data that can be entered in a database, and thus we have witnessed an enormous quantitative expansion of entered data, each in turn being defined with a highliy differentiated set of attributes. This has entailed a growing difficulty in our abililty to obtain a perception of the whole.

What I have called an explosion of the tabular model brought as a consequence the dimming of our ability to grasp the database as such: we have no problem in entering the fragments, but have growing difficulties in seeing how they hold together. Neither tabularity nor its graphic representations (bar histograms, pie charts, etc.) could any longer be seen as suited to their original task. And yet the whole beckons.

It is to meet this challenge, even if not expressed in these terms, that one has come to experiment with new approaches to visualization, in particular what is known as network analysis. We can rightfully say that visualization has come to serve more and more as the new gateway to perception.

~ contemplation (Maryanne)

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A reductive vision

freuquency compjutatinos as restrctured summation of fragments
not really a “whole”

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