1. Electronic Visualization Laboratory, University of Illinois at Chicago PAVIS Pervasive Adaptive Visualization and Interaction Service Javid Alimohideen Electronic Visualization Laboratory University of Illinois at Chicago CHI 2006 Workshop on Information Visualization and Interaction Techniques for Collaboration Across Multiple Displays Montreal, Canada 04/22/06 – 04/23/06

2. Electronic Visualization Laboratory, University of Illinois at Chicago Motivation & Goal U.S. Army Pantheon Project requirements –Process real-time sensor data to predict changes –Distribute information to variety of display platforms (from PDAs to tiled displays) Challenging for an application developer PAVIS intends to automatically generate resource-aware visualizations & context sensitive user-interfaces that are best suited for the display device using rule-based techniques.

3. Electronic Visualization Laboratory, University of Illinois at Chicago Related Work “An architecture for rule based visualization” incorporates perceptual rules into the visualization process (Rogowitz & Trenish, 1993, IBM Thomas J. Watson Research Center ) SUPPLE – considers interface generation as an optimization problem (Gajos & Weld, 2004, University of Washington) iCrafter – uses hand created templates (Shankar et al, 2001, Stanford University) XIML – user specified layout constraints (Puerta & Eisenstein, 2002, RedWhale Software, Palo Alto, CA) Thinlet – XML based JAVA GUI toolkit (Robert Bajzat, 2002) Cannot be used for Wall-sized displays, better knowledge of UI development is required, does not support both UI and visualization generation.

4. Electronic Visualization Laboratory, University of Illinois at Chicago PAVIS - Architecture Main source of data (E.g. 830 Chicago Highway sensor data, alerts, weather affecting traffic conditions) Main source of data (E.g. 830 Chicago Highway sensor data, alerts, weather affecting traffic conditions) Display size, device type, network bandwidth Data and device specific rules are efficiently merged and filtered Data and device specific rules are efficiently merged and filtered Data filtering on raw data based on filtered rules Generic built-in UI rule’s are tailored to device’s capabilities based on filtered rules Scalable rendering to render data from few thousand pixels to several hundreds of mega pixels

5. Electronic Visualization Laboratory, University of Illinois at Chicago Research Issues What are the key constraints of each operating platform? –Constraints include display size, user input device (mouse, keyboard, num pad or touch screen), network bandwidth –Constraints dictate the tasks that are most reasonable. (E.g. PDAs primarily receive alert information rather than high-resolution images) How does one build a Mathematical Rule based model using these constraints? How might a rule-based system act? –Relative salience of the data attributes determines which is crucial data for each platform (E.g. Alerts only for PDAs) –Choice of control widgets (E.g. List Menu for a PDA, Pie Menu for a Desktop) –Interaction Techniques (E.g. Zone zoom for zoom/pan, magic lens for more detail) –Interaction devices (E.g. Pointing interfaces for a Tiled display, mouse on a desktop, num pad on a cell phone) An alternative approach is to Design UI and Visualization scheme that makes sense for all display sizes –Smart widget set that could scale across displays well –Scalable Visualization widgets

6. Electronic Visualization Laboratory, University of Illinois at Chicago Current Status Implemented initial proof-of-concept: a client/server system that serves multiple PDA clients, and tiled displays with minimal data filtering techniques and generation of visualizations.

7. Electronic Visualization Laboratory, University of Illinois at Chicago Acknowledgments Supported in part by U.S. Army Pantheon Project and National Science Foundation Contact Information Javid Alimohideen javid@evl.uic.edujavid@evl.uic.edu Jason Leigh spiff@uic.eduspiff@uic.edu Electronic Visualization Laboratory University of Illinois at Chicago http://www.evl.uic.edu/pavis Thank You

8. Electronic Visualization Laboratory, University of Illinois at Chicago Rule Example Ex: if (alerts) cluster(alerts) if (events) display events if (events > alerts) highlight events else highlight alerts if (device type cellphone) map events to numpad else if (device type desktop) map events to mouse events else if (device type tiled display) map events to keyboard Cluster(alerts) { If (display size is small) cluster size = 10 else cluster size = 3 }

9. Electronic Visualization Laboratory, University of Illinois at Chicago PAVIS related Research Questions How much can an application benefit from a system like PAVIS ? –Reduces wok load for the application developer –Code reuse –Easy deployment –UI / Viz changes transparent to Client Other possible domains ? Is the generated layout and visualization optimal ?