1 / 10

Multi-Dimensional Data Visualization Techniques for Enhanced Information Design

This assignment explores multi-dimensional data visualization approaches as discussed in CS5984 with foundational readings including concepts from "Externalizing Abstract Mathematical Models" and "Movable Filters." Students must gather a dataset of at least 1,000 items with a minimum of 6 attributes, visualizing it using a minimum of 2 different tools such as Spotfire or Parallel Coordinates. The report will assess usability and dynamics, informed by notable sources like "Table Lens" and "Worlds within Worlds." The focus is on data interaction and insights from complex datasets.

alma
Download Presentation

Multi-Dimensional Data Visualization Techniques for Enhanced Information Design

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Multi-Dimensional Data Visualization 2 cs5984: Information Visualization Chris North

  2. Assignment • Read for Tues: • Tweedie&Spence, “Externalizing Abstract Mathematical Models” (Influence Explorer), p276 • ravi, mark • Keim, “VisDB”, p126 • ameya, sanjini • Read for Thurs: • Fishkin, “Movable filters” (Magic Lens), p253 • prasuna, umer • Doan, “Query Previews”, web page • aarthi, ameya

  3. Homework #1: Due Thurs Feb 1 • Get some data (>=1000 items, >=6 attributes) not demo data! • Visualize it (>=2 tools) • Spotfire, Advizor/SeeIt, Table Lens, Parallel Coordinates • Written report (2-3 pages) • individual • data: • X Usability – Rex hartson • eye-tracking - Debby hix -satya, prasuna • Biotech – lenny Heath, naren R -margaret (maellis1), maulik, sumithra • NASA - Layne watson -sanjini, aarthi • dept - Cliff Shaffer -purvi • univ - Dennis kafura • software eng - Steve Edwards -umer • survey data - Jan lee -dilshad

  4. Today • Rao, “Table Lens”, p343,597 • marty e, purvi

  5. Today • Feiner, “Worlds within Worlds”, p96 • Me, Ajay

  6. Nested Axes • (a,b,c,d) • (1,2,3,4) B B D C A A

  7. Nested Axes • (a,b,c,d): d=f(a,b,c) • E.g. 2=f(1,2,0) B B D ? C A A

  8. 7d: value=f(strike,spot,time,domestic,foreign,volatility) • Max D = 6 ? > 1 nesting gets cumbersome • Not much on its value to user • Apply to 2D display? • No overview of data • Good dynamics • Great for functions, not good for data points

More Related