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MDST 3705 2012-03-05 Databases to Visualization

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  • 1. From Database to Visualization Prof AlvaradoMDST 3705 5 March 2013

2. Business Quiz 2 to be posted this evening Covers everything between the last quiz andlast week Database theory and practice 3. Review Last week, we explored the idea of thedatabase as a symbolic form and genre The Database is a mode of representationcomparable to such things a linear perspective inpainting and the novel in writing The Database has certain representationalqualities Everything is a list (like an array) Order does not matter No inherent beginning or end Endlessly reconfigurable (SELECT, JOIN, etc.) 4. Review The Database stands in contrast tonarrative Traditional narrative is sequential and fixed Endings matter; novels have an arc. The Database reverses the relationshipbetween paradigm and syntagm Traditional works are final products of aprocess that is hidden and forgotten The products of a database are ephemeraland contingent the database itself is thething 5. Review Databases have a logic that is used in thearts Stories in which the order of events orperspectives are mixed up. Manovich calls thedatabase logic An example is the film, Man with a MovieCamera Databases can be more effective thanbooks in organizing works of art andliterature E.g. The Whitman Project 6. Vertovs film shows theJust as we saw that Linearrelationship betweenPerspective and the Novel goDatabase and Montage together 7. Data(bases) can be visualizedMore than that, they lend themselves to visualization Lets look at a couple of examples 8. A radial network graph from data scraped from Pandora, beginning with the Beatles 9. A force directed network graph of data scraped from Pandora, beginning with ElvisCostello 10. These network visualizations show thedatabase as a genre a way of representing informationCompare them to a catalog of musicalartists in a book (itself a kind of database) 11. A databaserecorddepicted as akind of text 12. The examples also showthe database as a way tounderstand genre 13. What is visualization? 14. a mapping between discrete data and a visual representation (Manovich)ora mapping of information in logical form to visual form 15. Manovich defines two types: Information VisualizationMedia Visualization 16. Statistics and information visualization were invented in the 18th century. This was linked to the rise of nation states and bureaucracyWilliamPlayfair 17. The result ofnations becomingaware of data ... 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. Here are some examples 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. http://www.visionlearning.com/library/large_images/image_4108.png 21. http://dougmccune.com/blog/wp- 22. http://www.economist.com/images/20071222/5107CR1B.jpg 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.http://www.math.yorku.ca/SCS/Gallery/images/priestley.gif 24. Minards maphttp://cartographia.files.wordpress.com/2008/05/minard_napoleon.png 25. http://cartographia.files.wordpress.com/2008/05/minard-full.jpg 26. http://commons.wikimedia.org/wiki/File:Minard-carte-viande-1858.png 27. The difference is that information visualizationsreveal patterns in the data,whereas info graphics usepatterns to present a pointor to present an idea 28. Media Visualizations are not essentiallyreductive, and they use color as much asspace 29. Time Magazine coversbetween 1923 and 2009 Data points are the objects themselves Color emerges as a key dimension Sequencing -- "cultural time series" 30. What can you learn from thisvisualization? 31. A million manga pages 32. Rothko and Mondrian 33. Not all visualizations areinformation visualizations inManovichs sense ... The following are info graphics 34. The Odyssey 35. The History of Science Fiction 36. Rebecca Blacks "Friday" 37. Whats the big difference? 38. Information and media visualizations are generated algorithmically Info graphics tend to be hand madecreations (although they may emulatealgorithms)The former exemplify Manovichs principle that databases generate works in this case, visualizations 39. Are information and media visualizationsmore truthful than information graphics? 40. graphesis 41. graphesisInformation embodied inmaterial form 42. graphesisOpposite of mathesis Science, math asuniversal language 43. Think of the relationship between geometryand algebraDatabase: Visualization :: Algebra : Geometry Which is more real? Which depends on theother? 44. Can we imagine what a point is without visualizing it?Is information separable from matter? 45. graphesisthe basis of mathesis 46. Media are always embedded in culture. Science was made possible by exact copy printing, a visual language (Ivins 1953)http://21st.century.phil-inst.hu/2002_konf/Nyiri/web_ivins.JPG 47. These images are bothbeautiful and effectiveAs digital scholars, our jobis to learn how to read,review, and produce them 48. The theory of graphesisteaches us that imageshave an epistemology, orcognitive style 49. Paradoxes Computers are based on mathesis, orlogico-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 bevisualized with forms that have a rhetoric 50. http://oneparticularwave.files.wordpress.com/2006/11/escher.gif 51. All visualization involvestransformationRaw Data Data Models Queries Arrays VisualArrangements 52. The final transformation The visual product encodes a series oftransformations from raw data to visualdesign A key element of this design is the use ofspace Space is complexit involves theconcepts of dimension, location, distance,and shape Each visualization uses these elementsdifferently 53. What is transformation? Review Examples 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 containcontent 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. http://studio1.shanti.virginia.edu/~rca2t/dataesthetics/03-29/v4.ph 56. Patterns of Transformation (ii) Network Graphs Purpose: Creates a network of relationships Space not uniformdistance and location of nodesrequire 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 foreach principle Convert array into PNG, SVG, etc. 57. http://studio1.shanti.virginia.edu/~rca2t/dataesthetics/04-26/graph-main.php 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. http://studio1.shanti.virginia.edu/~rca2t/dataesthetics/04-21/ex-04-pviz-matrix.php 60. Patterns of Transformation (iv) Arcs and Circles Purpose: Creates a 2D dimensions, with 1dimension metric, the other not Only an X axis with connections in qualitativespace Method: Same as network graphs Visualize using Protovis library 61. http://studio1.shanti.virginia.edu/~rca2t/dataesthetics/04-21/ex-04-pviz-arc.php 62. Patterns of Transformation (v) Hand-made Purpose: Creates a free-form qualitativespace Method: Draw!

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