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Big	
  Data	
  in	
  Search	
  of	
  Meta	
  Data	
  
Prof	
  Dr	
  Dirk	
  Baecker	
  
Content	
  Strategy	
  Forum	
  Conference,	
  
Frankfurt	
  am	
  Main,	
  1-­‐3	
  July	
  2014	
  
Metadata	
  Defined	
  
	
  
Metadata	
  are	
  not	
  just	
  about	
  
•  addresses,	
  
•  (strong	
  and	
  weak)	
  
connecHons,	
  
•  (high	
  and	
  low)	
  frequencies,	
  
•  structural	
  holes,	
  	
  
•  bridges,	
  	
  
•  and	
  structural	
  equivalencies	
  
(network	
  analysis)	
  
	
  
but	
  also	
  about	
  any	
  kind	
  of	
  data	
  
able	
  to	
  sort	
  data	
  (Bagley	
  1968).	
  
	
  
Which	
  means	
  that	
  you	
  have	
  to	
  
know	
  how	
  to	
  ask	
  in	
  order	
  to	
  be	
  
able	
  to	
  find	
  out.	
  
2	
  
E.g.,	
  the	
  Influence	
  
Matrix	
  
	
  
Prob	
  (ht
(c')	
  |	
  ht-­‐1
(1),	
  …,	
  ht-­‐1
(C))	
  
	
  
with	
  h	
  =	
  hidden	
  behavior	
  
of	
  a	
  person	
  c	
  of	
  a	
  group	
  of	
  
people	
  C.	
  
3	
  
New	
  York	
  2014	
  
Or	
  else,	
  an	
  Ac<on	
  
Matrix	
  
	
  
Prob	
  (ht
(c')	
  |	
  ht-­‐1
(1),	
  …,	
  ht-­‐1
(C)|	
  
ht+1
(1),	
  …,	
  ht+1
(C))	
  
	
  
h	
  =	
  hidden	
  behavior	
  
determined	
  by:	
  
•  purpose/system	
  and	
  
•  future/evoluHon,	
  
within	
  the	
  limits	
  of:	
  
•  complexity/
environment,	
  
with	
  t+1	
  =	
  unknown,	
  C	
  =	
  
fluctuaHng,	
  and	
  Prob	
  =	
  
uncertain.	
  
4	
  
A	
  Metadata	
  Matrix	
  
•  A	
  Calculus	
  of	
  Limits.	
  
•  Behavior	
  is	
  unpredictable	
  
because	
  it	
  is	
  structure-­‐
determined	
  –	
  and	
  you	
  don't	
  
know	
  which	
  structure	
  
prevails.	
  
•  Content	
  strategy	
  is	
  about	
  
possible,	
  yet	
  improbable	
  in-­‐
form-­‐ac4on.	
  
•  Pick	
  your	
  favorite	
  observer.	
  
•  Ask	
  for	
  metadata	
  that	
  let	
  
you	
  correct	
  your	
  
expectaHons	
  of	
  observers'	
  
behavior.	
  
5	
  

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Big Data in Search of Meta Data

  • 1. Big  Data  in  Search  of  Meta  Data   Prof  Dr  Dirk  Baecker   Content  Strategy  Forum  Conference,   Frankfurt  am  Main,  1-­‐3  July  2014  
  • 2. Metadata  Defined     Metadata  are  not  just  about   •  addresses,   •  (strong  and  weak)   connecHons,   •  (high  and  low)  frequencies,   •  structural  holes,     •  bridges,     •  and  structural  equivalencies   (network  analysis)     but  also  about  any  kind  of  data   able  to  sort  data  (Bagley  1968).     Which  means  that  you  have  to   know  how  to  ask  in  order  to  be   able  to  find  out.   2  
  • 3. E.g.,  the  Influence   Matrix     Prob  (ht (c')  |  ht-­‐1 (1),  …,  ht-­‐1 (C))     with  h  =  hidden  behavior   of  a  person  c  of  a  group  of   people  C.   3   New  York  2014  
  • 4. Or  else,  an  Ac<on   Matrix     Prob  (ht (c')  |  ht-­‐1 (1),  …,  ht-­‐1 (C)|   ht+1 (1),  …,  ht+1 (C))     h  =  hidden  behavior   determined  by:   •  purpose/system  and   •  future/evoluHon,   within  the  limits  of:   •  complexity/ environment,   with  t+1  =  unknown,  C  =   fluctuaHng,  and  Prob  =   uncertain.   4  
  • 5. A  Metadata  Matrix   •  A  Calculus  of  Limits.   •  Behavior  is  unpredictable   because  it  is  structure-­‐ determined  –  and  you  don't   know  which  structure   prevails.   •  Content  strategy  is  about   possible,  yet  improbable  in-­‐ form-­‐ac4on.   •  Pick  your  favorite  observer.   •  Ask  for  metadata  that  let   you  correct  your   expectaHons  of  observers'   behavior.   5