MDST 3705 2012-03-05 Databases to Visualization


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  • Note that statistics and visualization rise togetherNOT A BAD THING
  • mashup
  • mashup
  • See for full image
  • What is graphesis?
  • What is it the opposite of?
  • Mathesis = ontology = foundation of computing
  • Note that statistics and visualization rise togetherNOT A BAD THING
  • Note that statistics and visualization rise togetherNOT A BAD THING
  • MDST 3705 2012-03-05 Databases to Visualization

    1. 1. From Database to Visualization Prof Alvarado MDST 3705 5 March 2013
    2. 2. Business• Quiz 2 to be posted this evening – Covers everything between the last quiz and last week – Database theory and practice
    3. 3. Review• Last week, we explored the idea of the database as a ―symbolic form‖ and ―genre‖ – The Database is a mode of representation comparable to such things a linear perspective in painting and the novel in writing• The Database has certain representational qualities – Everything is a list (like an array) – Order does not matter – No inherent beginning or end – Endlessly reconfigurable (SELECT, JOIN, etc.)
    4. 4. Review• The Database stands in contrast to narrative – Traditional narrative is sequential and fixed – Endings matter; novels have an arc.• The Database reverses the relationship between paradigm and syntagm – Traditional works are final products of a process that is hidden and forgotten – The products of a database are ephemeral and contingent – the database itself is the thing
    5. 5. Review• Databases have a logic that is used in the arts – Stories in which the order of events or perspectives are mixed up. Manovich calls the ‗database logic‘ – An example is the film, Man with a Movie Camera• Databases can be more effective than books in organizing works of art and literature – E.g. The Whitman Project
    6. 6. Vertovs film shows the Just as we saw that Linearrelationship between Perspective and the Novel goDatabase and Montage together
    7. 7. Data(bases) can be visualizedMore than that, they lend themselves to visualization Let‘s look at a couple of examples …
    8. 8. A radial network graph from data scraped from Pandora, beginning with the Beatles
    9. 9. A force directed network graph of data scraped from Pandora, beginning with ElvisCostello
    10. 10. These network visualizations show the database as a genre – a way of representing information Compare them to a catalog of musicalartists in a book (itself a kind of database)
    11. 11. A databaserecorddepicted as akind of text
    12. 12. The examples also showthe database as a way to understand genre
    13. 13. What is visualization?
    14. 14. ―a mapping between discrete data and a visual representation‖ (Manovich) ora mapping of information in logical form to visual form
    15. 15. Manovich defines two types: Information Visualization Media Visualization
    16. 16. Statistics and information visualization were invented in the 18th century. This was linked to the rise of nation states and bureaucracyWilliamPlayfair
    17. 17. The result ofnations becomingaware of data ...
    18. 18. According to Manovich, the salient features of information visualization are(1) The reduction of data items to points, lines, etc. and(2) the use of space (size, shape, etc.) as the primary vehicle of representation Color is used, but as an embellishment
    19. 19. Here are some examples …
    20. 20. William Playfair (1786) The Commercial and PoliticalAtlas: Representing, by Means of Stained Copper-Plate Charts, the Progress of the Commerce,Revenues, Expenditure and Debts of England duringthe Whole of the Eighteenth Century.
    21. 21.
    22. 22.
    23. 23. Joseph Priestleys life-time graph of the lifespans offamous people. One of the first graphical time lines.Joseph Priestly, A Chart of Biography, 1765.
    24. 24. Minard’s map
    25. 25.
    26. 26.
    27. 27. The difference is that information visualizationsreveal patterns in the data,whereas info graphics usepatterns to present a point or to present an idea
    28. 28. Media Visualizations are not essentiallyreductive, and they use color as much as space
    29. 29. Time Magazine coversbetween 1923 and 2009 Data points are the objects themselves Color emerges as a key dimension Sequencing -- "cultural time series"
    30. 30. What can you learn from this visualization?
    31. 31. A million manga pages
    32. 32. Rothko and Mondrian
    33. 33. Not all visualizations areinformation visualizations in Manovichs sense ... The following are ―info graphics‖
    34. 34. The Odyssey
    35. 35. The History of Science Fiction
    36. 36. Rebecca Blacks "Friday"
    37. 37. What’s the big difference?
    38. 38. Information and media visualizations are generated algorithmically Info graphics tend to be hand made creations (although they may emulate algorithms)The former exemplify Manovich‘s principle that databases generate works – in this case, visualizations
    39. 39. Are information and media visualizationsmore truthful than information graphics?
    40. 40. graphesis
    41. 41. graphesisInformation embodied inmaterial form
    42. 42. graphesisOpposite of mathesis –Science, math asuniversal language
    43. 43. Think of the relationship between geometry and algebraDatabase: Visualization :: Algebra : Geometry Which is more real? Which depends on the other?
    44. 44. Can we imagine what a point is without visualizing it?Is information separable from matter?
    45. 45. graphesisthe basis of mathesis
    46. 46. Media are always embedded in culture. Science was made possible by exact copy printing, a visual language (Ivins 1953)
    47. 47. These images are bothbeautiful and effectiveAs digital scholars, our jobis to learn how to read,review, and produce them
    48. 48. The theory of graphesisteaches us that imageshave an epistemology, or―cognitive style‖
    49. 49. Paradoxes• Computers are based on mathesis, or logico-mathematical thinking• And visualization is based on computing• Ergo, mathesis precedes graphesis• But, mathesis rests on graphesis – The iconography of mathematical symbols – The products of mathesis must always be visualized with forms that have a rhetoric
    50. 50.
    51. 51. All visualization involves transformationRaw Data  Data Models Queries  Arrays  Visual Arrangements
    52. 52. The ―final‖ transformation• The visual product encodes a series of transformations from raw data to visual design• A key element of this design is the use of space• Space is complex—it involves the concepts of dimension, location, distance, and shape• Each visualization uses these elements differently
    53. 53. What is transformation? Review Examples
    54. 54. Patterns of Transformation (i)• Image Grids (aka Image Graphs) – Purpose: Creates 2D qualitative space • Space is uniform, Cartesian • ―Points‖ are actually not atomic, but contain content • Designed to show ―hot spots‖ – Method: • Identify X and Y in which to plot objects of type A • Create query to generate A, X and Y columns • Convert query data into 3D array $DATA[$X][$Y] = $A • Convert array into HTML
    55. 55.
    56. 56. Patterns of Transformation (ii)• Network Graphs – Purpose: Creates a network of relationships • Space not uniform—distance and location of nodes require interpretation – Method: • Identify nodes and principle of relationship (e.g. container) • Create query to generate nodes and principle • Convert query into NODE and EDGE arrays • Convert arrays data into Cartesian Product for each principle • Convert array into PNG, SVG, etc.
    57. 57.
    58. 58. Patterns of Transformation (iii)• Adjacency Matrix – Purpose: Creates a 2D space • But X and Y are ―self similar‖ – Method: • Identify X and Y • Create query to generate X and Y columns • Convert query data into 2D array • Convert array into HTML
    59. 59.
    60. 60. Patterns of Transformation (iv)• Arcs and Circles – Purpose: Creates a 2D dimensions, with 1 dimension metric, the other not • Only an X axis with connections in qualitative space – Method: • Same as network graphs • Visualize using Protovis library
    61. 61.
    62. 62. Patterns of Transformation (v)• Hand-made – Purpose: Creates a free-form qualitative space – Method: • Draw!