BIG DATA FORSIGHT

2
3
4
5

Michele Banko und Eric Brill 2001: http://acl.ldc.upenn.edu/P/P01/P01-1005.pdf
6

http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram
DATA SCIENCE 101
1. Daten erzeugen Sammeln
2. Daten ablegen / abrufen (=wiederfinden)
3. Daten bereinigen
4. Daten analysi...
8

Quelle: Google Tends http://www.google.com/trends/explore#q=big%20data,%20business%20intelligence
9
Google Correlate www.google.com/trends/correlate
10

Google Ngram Viewer http://books.google.com/ngrams + DB http://books.google.com/ngrams/datasets
11

Acerbi et al 2013 http://www.plosone.org/article/info:doi/10.1371/journal.pone.0059030
TEXTANALYSE
Wort
Aar
Aartalbahn
Aartalhalle
Abbild
Abbildung
aber
Abgaben

Limburg
1

1
2
7
1

abgegrenzten
Abgeordnete

1...
TEXTANALYSE
jbenno X tirsales: 0.875271765478
jbenno X christiansoeder: 0.867212021813
jbenno X afelia: 0.846274132298
jbe...
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15

Eric Fischer „See something or say something“http://www.flickr.com/photos/walkingsf/5935471000/in/set-7215762714031074...
As we know,
There are known knowns.
There are things we know we know.
We also know
There are known unknowns.
That is to sa...
known knowns

Dashboards:
„Data puking“

known unknowns

unknown unknowns

17

Modellings:
„Analysis throwing“

Big Data:
...
18
19
20
21
22
Open Foresight
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Open Foresight, Streetfighting Data Science, Open Data

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Open Foresight

  1. 1. BIG DATA FORSIGHT 2
  2. 2. 3
  3. 3. 4
  4. 4. 5 Michele Banko und Eric Brill 2001: http://acl.ldc.upenn.edu/P/P01/P01-1005.pdf
  5. 5. 6 http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram
  6. 6. DATA SCIENCE 101 1. Daten erzeugen Sammeln 2. Daten ablegen / abrufen (=wiederfinden) 3. Daten bereinigen 4. Daten analysieren 5. Daten visualisieren 7
  7. 7. 8 Quelle: Google Tends http://www.google.com/trends/explore#q=big%20data,%20business%20intelligence
  8. 8. 9 Google Correlate www.google.com/trends/correlate
  9. 9. 10 Google Ngram Viewer http://books.google.com/ngrams + DB http://books.google.com/ngrams/datasets
  10. 10. 11 Acerbi et al 2013 http://www.plosone.org/article/info:doi/10.1371/journal.pone.0059030
  11. 11. TEXTANALYSE Wort Aar Aartalbahn Aartalhalle Abbild Abbildung aber Abgaben Limburg 1 1 2 7 1 abgegrenzten Abgeordnete 1 1 1 Abgeordneter abgerissen Abgerufen abgeschlossen abgetrennt Abitur Abschluss Abschnitt 12 Abschnitten Stockdorf 1 3 3 1 1 2 1 2 1 1 Taunusstein 2 1 1 1 Cos(Taunusstein, Stockdorf) 0,75 Cos(Stockdorf, Limburg) 0,81 Cos(Taunusstein, Limburg) 0,76
  12. 12. TEXTANALYSE jbenno X tirsales: 0.875271765478 jbenno X christiansoeder: 0.867212021813 jbenno X afelia: 0.846274132298 jbenno X sekor: 0.839620669666 jbenno X sommercharlie: 0.798025077486 jbenno X zinken: 0.762690512216 jbenno X djanecek: 0.746300186002 jbenno X holadiho: 0.718939291016 jbenno X furukama: 0.674379861632 jbenno X schlenzalot: 0.664230808291 jbenno X dr_ultra: 0.627733894581 jbenno X praetorius: 0.586885278055 Analyse 2012 von Benedikt Köhler ( http://blog.metaroll.de ) 13
  13. 13. 14
  14. 14. 15 Eric Fischer „See something or say something“http://www.flickr.com/photos/walkingsf/5935471000/in/set-72157627140310742 and „Locals and Tourists“http://www.flickr.com/photos/walkingsf/4671578001/in/set-72157624209158632
  15. 15. As we know, There are known knowns. There are things we know we know. We also know There are known unknowns. That is to say We know there are some things We do not know. But there are also unknown unknowns, The ones we don't know We don't know. Donald Rumsfeld 16
  16. 16. known knowns Dashboards: „Data puking“ known unknowns unknown unknowns 17 Modellings: „Analysis throwing“ Big Data: „Data democracy“ Avinash Kaushik
  17. 17. 18
  18. 18. 19
  19. 19. 20
  20. 20. 21
  21. 21. 22

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