1. Softwaretechnologie für Fortgeschrittene Teil Thaller Stunde VI: Information revisited … Köln 9. Januar 2014

2. Beobachtungen: Bilder und Texte - 2013

3. Süddeutsche Zeitung, Nr. 58, 9./10. März 2013, Wochenendbeilage, p. V2/2

5. Bilder und Texte sind 2013 so ubiquitär, dass sie in modernen Informationssystemen nicht mehr sinnvoll getrennt gehandhabt werden können. Hintergrundthese

6. Bilder und Texte in geisteswissenschaftlichen Informationssystemen: State of the Art

7. κλειω – Bild / Textverknüpfungen, Stand 1992 Text – Bild – Editoren

8. Meta Image, http://www2.leuphana.de/meta-image/ (11.12.2013) Text – Bild – Editoren

9. http://www.hucompute.org/ressourcen/ehumanities-desktop (11.12.2013) Text – Bild – Editoren

10. http://www.hucompute.org/ressourcen/ehumanities-desktop (11.12.2013) Text – Bild – Editoren

11. Magisterarbeiten J. Schnasse (2006), E. Weiper (2007) Text – Bild – Editoren

12. TILE (Text Image Linking Evironment) http://mith.umd.edu/tile/ (11.12.2013) Text – Bild – Editoren

13. TextGrid http://www.textgrid.de/ (11.12.2013) Text – Bild – Editoren

14. ELAN http://www.lat-mpi.eu/tools/elan/ (11.12.2013) Text – Bild – Editoren

15.  Text- und Bildabschnitte, sowie Punkte in strukturierten Wissensdarstellungen (Ontologien) können in beliebiger Granularität miteinander verbunden werden.  Textabschnitte können aus Mengen diskontinuierlicher Abschnitte beliebiger Granularität bestehen.  Bildabschnitte können aus Mengen diskontinuierlicher Abschnitte beliebiger Form bestehen.  Es stehen Werkzeuge zur Formerkennung zur Verfügung.  Suchabfragen können datentypspezifische Frageformen aller drei Domänen miteinander verbinden.  Alle Funktionen sind ohne explizite Installation von Clientsoftware über Standardbrowser zugänglich.  Beschreibungen können kollaborativ nach unterschiedlichen Rollenmodellen angelegt werden. Idealtypische Anforderungen …

16. Bilder und Texte - Information

17. What is a text within the Digital Humanities, or some of them at least? Manfred Thaller, Universität zu Köln Digital Humanities 2012, July 20 th 2012

18. Information I

19. Claude Shannon: "A Mathematical Theory of Communication", Bell System Technical Journal, 1948. Shannon 19

20. The fundamental problem of communication is that of reproducing at one point either exactly or approximately a message selected at another point. (Shannon, 1948, 379) Shannon 20

21. Shannon It is wet outside. It must be raining … 21

22. The fundamental problem of communication is that of reproducing at one point either exactly or approximately a message selected at another point. Frequently the messages have meaning; that is they refer to or are correlated according to some system with certain physical or conceptual entities. These semantic aspects of communication are irrelevant to the engineering problem. (Shannon, 1948, 379) Shannon 22

23. Shannon It is wet outside. It must be raining … 23

24. Shannon It is wet outside. It is wet outside … 24

25. Shannon 1011 1000 1001 25

26. „Ladder of Knowledge“ wisdom knowledge information data 26

27. Information 27

28. Data 28

29. Data are stored. E.g.: 22°C. Information are data interpreted within a context: "In this lecture hall the temperature is 22°C". This context is fixed and identical for all recipients of information. Data  Information 29

30. Knowledge is the result of a more complex process. E.g. the decision, derived from the room temperature of 22 ° centigrade, to get out of your jacket; or not. This context is different between recipients of information. Information  Knowledge 30

31. Data 22 ° C 22 ‘00000000 00010110’ So … Information 22 ° C in lecture hall M 22 ° 22 [ NOT ASCII { 0, 22 } ] 31

32. Langefors “Infological Equation”: original I = i (D, S, t) I ::= Information i() ::= interpretative process D ::= Data S ::= Previous knowledge t ::= time Börje Langefors, Essays on Infology, Studentliteratur: Lund, 1995 Langefors 32

33. Information II

34. Receiving information … The kink has been kiled. Oh, that was John II. Very strange spelling, even for that time.. 34

35. Receiving information … Oh, that was John II. Very strange spelling, even for that time.. Notice: We can not consult the sender any more …. ? 35

36. Langefors “Infological Equation”: original I = i (D, S, t) I ::= Information i() ::= interpretative process D ::= Data S ::= Previous knowledge t ::= time Börje Langefors, Essays on Infology, Studentliteratur: Lund, 1995 Langefors 36

37. Langefors “Infological Equation”: generalization 1 I 2 = i (I 1, S 2, t) I ::= Information i() ::= interpretative process D ::= Data S ::= Previous knowledge t ::= time Börje Langefors, Essays on Infology, Studentliteratur: Lund, 1995 Langefors 37

38. Receiving information … Oh, that was John II. Very strange spelling, even for that time.. Notice: We can not consult the sender any more …. ? 38

39. Receiving information … Or, was it 100 years earlier? Very strange spelling, even for that time.. Notice: We can not consult the sender any more …. ? 39

40. Langefors “Infological Equation”: generalization 2 I x = i (I x-1, S x, t) I ::= Information i() ::= interpretative process D ::= Data S ::= Previous knowledge t ::= time Börje Langefors, Essays on Infology, Studentliteratur: Lund, 1995 Langefors 40

41. Langefors “Infological Equation”: generalization 3 S x = s (I x-1, t) I ::= Information i() ::= interpretative process D ::= Data S ::= Previous knowledge, s() = knowledge generating process t ::= time Börje Langefors, Essays on Infology, Studentliteratur: Lund, 1995 Langefors 41

42. Langefors “Infological Equation”: generalization 4 I x = i (I x-α, S x-β, t) I ::= Information i() ::= interpretative process D ::= Data S ::= Previous knowledge t ::= time Börje Langefors, Essays on Infology, Studentliteratur: Lund, 1995 Langefors 42

43. Langefors “Infological Equation”: generalization 5 I x = i (I x-α, s(I x-β, t), t) I ::= Information i() ::= interpretative process D ::= Data S ::= Previous knowledge t ::= time Börje Langefors, Essays on Infology, Studentliteratur: Lund, 1995 Langefors 43

44. Data 22 ° C 22 ‘00000000 00010110’ Remember … Information 22 ° C in lecture hall M 22 ° 22 [ NOT ASCII { 0, 22 } ] 44

45. Changeable datatypes int myVariable; char myVariable; temperature myVariable; obj myVariable; myVariable.useAsInt(); myVariable.useAsChar(); myVariable.addInterpretation(temperature,Centigrade); 45

46. Notes: (1)If this is so, the assumption of Comp. Sci., that information is represented by structures on which algorithms operate, can be replaced by a more general understanding, according to which information is a state of a set of perpetually active algorithms. (2) Has that any practical meaning? Langefors 46

47. A practical interlude

48. ► Photoshop ► Planets: the problem 48

49. png tiff Extractor Comparator image info 2 image info 1 the same? Format conversion png rulestiff rules Planets: the vision 1 49

50. Obj 1 Obj 2 Extractor Comparator object info 2 object info 1 the same? Format conversion rule set 1rule set 2 Planets: the vision 2 50

51. Planets: the vision 3 Obj 1 Obj 2 Extractor Comparator XCDL 2 XCDL 1 the same? Format conversion XCEL 1XCEL 2 Specification of „similiarity“ to be used: „comparator comparison [Language] “ (coco). Specification of „similiarity“ observed: „comparator results [Language] “ (copra). Abstract description of file content: „eXtensible Characterisation Definition Language“ (XCDL), able to describe the content of digital objects (=1 + n more files), processible by a software tool for further analysis. Machine readable form of a file format specification: „eXtensible Characterisation Extraction Language“ (XCEL), able to describe any machine readable format in a formal language, processible by a software tool for extraction of content as XCDL. 51

52. This is a text 54 68 69 73 20 69 73 20 61 20 74 65 78 74 … fontsize 48 unsignedInt8 Text in XCDL 52

53. 7A 11 9B 77 34 89 72 11 29 F4 DA 9C B2 23 56 93 86 83 82 65 … … title Ebstorf Mappa Mundi ASCII Image in XCDL 53

54. Generalizing the practical solution

55. Allows to make statements about the proximity of two objects on the "y" axis. Irrespective of the "shape" of the object. Dimensions: geometry 55

56. Allows to make statements about the proximity of two objects on the "y" axis. Irrespective of the “object" that is at the abstract position. Dimensions: textual / conceptual 56

57. Dimensions are by definition orthogonal. Dimensions can have any sort of metric:  Rational: { - ∞ … + ∞ }  Integer range: { 0 … 100 }  Nominal: { medieval, early modern, modern }  Image: {, }  … Dimensions: metrics 57

58. (1) Biggin (2) Biggin (3) Biggin (4) Biggin Which of the chunks are more similar to each other: (1) and (2) or (1) and (3)? Four texts … 58

59. … in a coordinate space. 59

60. Liber exodi glosatus An image in a textual coordinate space 60

61. Liber exodi glosatus An text in an image coordinate space 61

62. An image in a semantic coordinate space Bishop Cardinal Monk Priest Monk 62

63. Semantics in an image coordinate space Bishop Cardinal Monk Priest Monk 63

64. Generalization 1 Biggin Visualization {bold, italic} Interpretation {surname, topographic name} 64

65. Generalization 2 Series of atomic content tokens Conceptual dimension 1 Conceptual dimension 2 65

66. Generalization 3 { T, C 1, C 2 } 66

67. Generalization 4 { T, { C 1, C 2, …, C n } } 67

68. Generalization 5 { T, C n } (1) Texts are sequences of content carrying atomic tokens. (2) Each of these tokens has a position in an n-dimensional conceptual universe. 68

69. Generalization 6 { X, Y, C n } 69

70. Generalization 7 { T 1, T 2, C n } (1) Images are planes of content carrying atomic tokens. (2) Each of these tokens has a position in an n- dimensional conceptual universe. 70

71. Generalization 8 I ::= { { T 1, T 2, … T m }, C n } (1) Information objects are m-dimensional arrangements of content carrying atomic tokens. (2) Each of these tokens has a position in an n- dimensional conceptual universe. 71

72. Generalization 9 I ::= {T m, C n } (1) Information objects are m-dimensional arrangements of content carrying atomic tokens. (2) Each of these tokens has a position in an n- dimensional conceptual universe. (3) All of this, of course, is recursive … 72

73. Another practical interlude

74. Virtual Research Environments http://www.monasterium.net/  Virtuelles deutsches Urkundennetz (Virtual network of German charters) 74

75. digitization editing research transcription publication symbol manipulation teachteach A model of historical research

76. base image editorial coordinates semantic coordinates textual coordinates publication coordinates symbol coordinates didactic coordinates A model of historical research

77. Conclusion

78. Summary (1)All texts, for which we cannot consult the producer, should be understood as a sequence of tokens, where we should keep the representation of the tokens and the representation of our interpretation thereof completely separate. (2)Such representations can be grounded in information theory. (3)These representations are useful as blueprints for software on highly divergent levels of abstraction. 78

79. Thank you! manfred.thaller@uni-koeln.de 79