Another Word For It Patrick Durusau on Topic Maps and Semantic Diversity

March 5, 2014

On Data and Performance

Filed under: Art,Data,Topic Maps — Patrick Durusau @ 4:46 pm

On Data and Performance by Jer Thorp.

From the post:

Data live utilitarian lives. From the moment they are conceived, as measurements of some thing or system or person, they are conscripted to the cause of being useful. They are fed into algorithms, clustered and merged, mapped and reduced. They are graphed and charted, plotted and visualized. A rare datum might find itself turned into sound, or, more seldom, manifested as a physical object. Always, though, the measure of the life of data is in its utility. Data that are collected but not used are condemned to a quiet life in a database. They dwell in obscure tables, are quickly discarded, or worse (cue violin) – labelled as ‘exhaust’.

Perhaps this isn’t the only role for a datum? To be operated on? To be useful?

Over the last couple of years, with my collaborators Ben Rubin & Mark Hansen, we’ve been investigating the possibility of using data as a medium for performance. Here, data becomes the script, or the score, and in turn technologies that we typically think of as tools become instruments, and in some cases performers.

The most recent manifestation of these explorations is a performance called A Thousand Exhausted Things, which we recently staged at The Museum of Modern Art, with the experimental theater group Elevator Repair Service. In this performance, the script is MoMA’s collections database, an eighty year-old, 120k object strong archive. The instruments are a variety of custom-written natural language processing algorithms, which are used to turn the text of the database (largely the titles of artworks) into a performable form.

The video would have been far more effective had it included the visualization at all time with the script and actors.

The use of algorithms to create a performance from the titles of works reminds me of Stanley Fish’s How to Recognize a Poem When You See One. From my perspective, the semantics you “see” in data are the semantics you expect to see. What else would they be?

What I find very powerful about topic maps is that different semantics can reside side by side for the same data.

I first saw this in tweet by blprnt.

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