1. 1 ITM 734 Introduction to Human Factors in Information Systems Cindy Corritore cindycc@gmail.com Information Visualization

2. corritore, 734 overview increasingly common to actually have all of the data potentially available  how to map and use it becomes harder and harder challenges: world of the computer and data and world of the human  bridge between the intuitive, creative, experience and the digital, analytical Solution: Involve the user!

3. Corritore, 2005 challenges

4. Corritore, 2005 challenge 1 1. growing volume of data with declining information content  provision of data ever cheaper and available  our ability to consume information largely unchanged Key Issues: exploring, navigation, browsing, immersion/involvement of human and their perceptional apparatus

5. Corritore, 2005 challenge 1 interactive visualization interface for exploration of network fault data (network alarm data)  experienced network administrator looks for trends/patterns  interactive with filters

6. Corritore, 2005 challenge 1 large information spaces

7. Corritore, 2005 challenge 2 2. convert appropriate data to relevant data: analysis and interpretation  summarize and compress without signif. loss of content  complex data analysis tools and models for analysis hard to use  goal: human involvement in processing and analysis of data –experience and intuition

8. Corritore, 2005 challenge 2 visual correlation between lightning strikes & network alarms  time series movie

9. Corritore, 2005 challenge 3 managing abstract problems/intangibles against increasingly short timescales  build a building - can see the progress; intangibles hard to visualize  better informed decisions  goal: retain overview of abstract problem while providing for immediate visibility of changes

10. Corritore, 2005 challenge 3 software development  each sphere a module (diameter - size)  lines are func. calls  change requests mapped to rate of spin

11. Corritore, 2005 challenge 4 communicate a vision - wide audience and increasingly conceptual  wider, less specialist audience; mix of technical, business, customer  hence, must provide a shared experience picture is worth 1,000 words

12. Corritore, 2005 Goal: let human observe, manipulate, search, navigate, explore, filter, discover, understand, and interact with large volumes of data rapidly

13. Corritore, 2005 data types 1D  lists, words  http://www.textarc.org/Alice2inWindow.html - Alice in Wonderland http://www.textarc.org/Alice2inWindow.html  fisheye – http://businessethics.creighton.eduhttp://businessethics.creighton.edu 2D  map data (gis)  google earth (street view) moving 2D to 3D  smartmoney.com - http://www.smartmoney.com/maps/?nav=dropTab http://www.smartmoney.com/maps/?nav=dropTab

14. Corritore, 2005 data types 3D  scientific visualization (molecules, etc)  ThemeView - http://in-spire.pnl.gov/IN- SPIRE_Help/galaxy.html - shows documents and their relationshipshttp://in-spire.pnl.gov/IN- SPIRE_Help/galaxy.html –galaxy view –themeview  task manager – task manager

15. Corritore, 2005 data types 3D and file systems

16. Corritore, 2005 data types multi-dimensional  n-dimensional space – examples?  Spotfire – Decision GalleryDecision Gallery  Homefinder temporal  time lines (stock markets, health care) – HCI Lab for HemodialysisHemodialysis –Each row is a dialysis session with 50 parameters, time is X axis for session, z is time over sessions, Y is value of parameters

17. Corritore, 2005 data types temporal  variables over time River metaphor: look for themes in a document collection over time  Each attribute is mapped to a “current” in the “river”, flowing along the timeline Current width ~= strength of theme River width ~= global strength Color mapping (similar themes – same color family) Time line

18. Corritore, 2005 A company’s patent activity Event

19. Corritore, 2005 critique Strong points: Intuitive exploration of temporal changes and relations Evalutation + improvements Applicable to general attributes Weak points: Limited number of themes / attributes Interpolated values / outer attributes misleading No ability to reorder currents Performance issues

20. Corritore, 2005 spiral Example Spokes (months) and spiral guide lines (years) Planar spiral Distinguishable patterns (rainy season / 1984) Chimpanzees Monthly food consumption 1980-1988

21. Corritore, 2005 data types more temporal –  Time Searcher (http://www.cs.umd.edu/hcil/timesearcher/vi deos/ts2_HCILsoh2005R.html) – moviehttp://www.cs.umd.edu/hcil/timesearcher/vi deos/ts2_HCILsoh2005R.html  lifelines – (IE) http://www.cs.umd.edu/hcil/lifelines/latestde mo/chi.html http://www.cs.umd.edu/hcil/lifelines/latestde mo/chi.html

22. Corritore, 2005 data types trees  hierarchies (file structure)  Magnifind – on desktop  lexusnexus – had one :(  Cop - http://ai.bpa.arizona.edu/COPLINK/dem o/Visualization.htm http://ai.bpa.arizona.edu/COPLINK/dem o/Visualization.htm  Visual Thesaurus Visual Thesaurus

23. Corritore, 2005 challenges multiple data input combine visual and text show relationships large information spaces – overview then details collaboration? navigation must be accurate all elements must be interactive new paradigms ……

24. C.L. Corritore24 2D visualization Pad - 2D visualization tool (turn down colors)  widgets  presentation Counterpoint (show presentation BIB 2004)