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Enhancing Information Visualization through Interactive Techniques

Explore the essential techniques of interaction in information visualization, from retrieving and sorting data to identifying anomalies and correlations. Discover the impact of interactive design on data exploration and analysis. Delve into Shneiderman’s mantra and the significance of user engagement for meaningful visualizations.

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Enhancing Information Visualization through Interactive Techniques

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  1. Lecture 15:Interaction March 28, 2013 COMP 150-2Visualization

  2. Admin • Update on Ben Shapiro’s data • Group Assignment Review • Incentivize projects? • April 25 schedule? • Invite department (and get pizzas) • Thoughts on group projects…

  3. Exercise • Name all the types of tasks that you’d like your visualization to do

  4. Analytic Activity in Information Visualization • Amar, Eagan, Stasko (2005) • Retrieve value • Filter • Compute derived value • Find extremum • Sort • Determine range • Characterize distribution • Find anomalies • Cluster • Correlate

  5. Analytic Activity in Information Visualization • Amar, Eagan, Stasko (2005) • Retrieve value • What are the values of attributes X, Y, Z in the data points A, B, C? • Filter • Which funds under-performed the S&P 500 last year? • Compute derived value • What is the average income of CS grad students? • Find extremum • Which car has the highest MPG? • Sort • Order the cars by horse power

  6. Analytic Activity in Information Visualization • Amar, Eagan, Stasko (2005) • Determine range • What is the length of this film? Who are the actors in this movie? • Characterize distribution • What is the age distribution of shoppers who purchase cars with 40+ MPG? • Find anomalies • Who are the outliers? • Cluster • Which cars are similar to each other in MPG, horse power, and price? • Correlate • Is there a relationship between horse power and MPG?

  7. Questions?

  8. Different Directions and Possibilities • Animated control • http://www.nytimes.com/interactive/2009/11/06/business/economy/unemployment-lines.html • Hypothesis testing • http://www.lasvegassun.com/gambling-addiction/slotmachine/ • Algorithm to generate 1 image • http://flowingdata.com/2011/03/02/german-defense-ministers-plagiarized-phd-dissertation-visualized/ • http://internetcensus2012.bitbucket.org/paper.html • http://moviegalaxies.com/ • Tell a story • http://www.guardian.co.uk/world/interactive/2013/feb/12/state-of-the-union-reading-level • http://www.datapointed.net/visualizations/color/men-women-color-names-d3/ • Be Epic! Use visualization to show something cool… • http://flowingdata.com/2010/12/22/epic-animation-in-google-docs/ • http://salavon.com/SpecialMoments/SpecialMoments.shtml • http://www.nytimes.com/interactive/2013/02/22/sunday-review/the-consensus-candidate.html • http://flowingdata.com/2009/05/14/pixel-city-computer-generated-city/

  9. Shneiderman’s Mantra • Ben Shneiderman, “The Eyes Have It: A Task by Data Type Taxonomy for Information Visualization”, 1996 IEEE Computer Society Press • “Overview first, zoom and filter, then details-on-demand”

  10. Interaction • What is visualization without interaction? • What is the role of interaction in visualization? • Is it • visualization + interaction? • interaction + visualization?

  11. Shneiderman’s Mantra • Overview first, zoom and filter, then details-on-demand • Overview • Visualize the data in its entirety • Zoom and filter • Let the user select and focus on the important stuff • Details on demand • Show what the user selected

  12. Zoom and Filter • What is the user really doing when they perform “zoom” or “filter”? • Interaction == • “direct manipulation and instantaneous change” (Becker et al. 1987) • “the communication between user and the system” (Dix et al. 1998)

  13. “Little Brother” • “Interaction is rarely the main focus of research efforts in the field, essentially making it the ‘little brother’ of [information visualization]” (Yi et al. InfoVis 2007) • As shown by Shneiderman, visualization == • Visual representation • Interaction

  14. “Little Brother” • In most visualization courses, • Visual representation is the focus • Interaction receives little attention • But interaction is just as important if not more so! • Lots of opportunities because few people are thinking about it

  15. Research • Designing completely new visual representations is hard • The domain is well covered • But given the same visualization, different interactions can allow different explorations, analyses, and discoveries • This is because interaction makes a visualization come “alive” • otherwise visualization is just a drawing on a piece of paper

  16. Exercise: • What’s wrong with a (static) stacked graph? • http://www.meandeviation.com/dancing-histograms/hist.html

  17. Questions?

  18. Technically… • What makes a visualization interactive? • In computer graphics, 12 frames per second is thought to be interactive because the animation will appear “smooth” to most people • 12 fps == 0.08 second per frame • Cognitively, • < 0.1 second : animation, continuity • < 1 second: system response, conversation break • < 10 second: cognitive response

  19. Interaction as Selection • In most interactive visualization systems, we think of the interaction as the ability to select or filter some data items • Shneiderman’s “zoom and filter” • What does that mean technically?

  20. Database Background • SQL Query. Example: • SELECT person • FROMdatasetTable • WHERE (weight > 130) AND • (weight < 180) AND • (height > 5.8) AND • (height < 6)

  21. Database and Visualization Height = 6 Height = 5.8 weight = 130 weight = 180 • This is the equivalent of a selection box in a scatter plot visualization

  22. Database and Visualization • For a specific type of interaction (e.g. select, filter, zoom, etc.), there is often a 1:1 mapping between the interactions and database queries • Homefinder Example

  23. Database and Visualization • There is a nice mapping between each visual element with an attribute in the database • SELECT latitude, longitude • FROMhomesTable • WHERE(distance > 5) AND • (distance < 10) AND • (price > 100,000) AND • (price < 300,000) AND • (bedrooms > 3) AND • (garage = TRUE) AND • (fireplace = FALSE)

  24. Query Relaxation • “Generalized Selection via Interactive Query Relaxation” (Heer et al. CHI 2008) • http://vis.berkeley.edu/papers/generalized_selection/

  25. Database Strength and Weakness • Strengths: • Generalizable • Separates data from visualization cleanly • SQL is a very stable language that is very complete for interacting with data stored in relational databases • Weaknesses: • Dynamic query construction is tricky • DB overhead • Speed is a problem • Network speed • DB searching speed • Parsing DB returned tuples

  26. Conjunctive Form • A selection / filtering / zooming interaction == database query • It can also be thought of as a conjunctive form • !(A1 V A2) ^ A3 V (A4 V A5 ^ A6) V … • where A1 could be the clause (price > 200)

  27. Conjunctive Form • Very similar to SQL query, but can be built in software • Has some of the similar limitations as SQL in that the clauses need to be pre-built • Such that a data attribute needs to be “hard-coded” to a visual element • Consider a slider that connects to two or more attributes (http://gravis.cs.unibas.ch/publications/2007/VIS07_Smith.pdf)

  28. Questions?

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