Accommodating Global Policies and User Preferences in Computer Supported Collaborative Systems

1
Accommodating Global Policies andUser
Preferences in Computer Supported Collaborative
Systems
Student Helmuth Trefftz Advisors Prof. Rick
Mammone Prof. Ivan Marsic Rutgers
University April, 2001
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Agenda

• Motivation
• Related Work
• Thesis Statement
• Experimental Setup
• Mathematical Model
• Preliminary Results
• Future Work
• Conclusions

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Motivation (1)

• We are multi-modal

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Motivation (1)

• We are multi-modal
• We acquire information through multiple
  modalities (sensory channels)
• Sight
• Sound
• Smell
• ..

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Motivation (1)

• We are multi-modal
• We acquire information through multiple
  modalities (sensory channels)
• Sight
• Sound
• Smell
• ..
• Most intense experiences involve multiple
  modalities (love)

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Motivation (2)

• Computer Systems become more and more multimodal

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Motivation (2)

• Computer Systems become more and more multimodal
• e-mail, chat

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Motivation (2)

• Computer Systems become more and more multimodal
• e-mail, chat
• chat with cartoons (Microsoft)

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Motivation (2)

• Computer Systems become more and more multimodal
• e-mail, chat
• chat with cartoons (Microsoft)
• CuSeeMe

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Motivation (2)

• Computer Systems become more and more multimodal
• e-mail, chat
• chat with cartoons (Microsoft)
• CuSeeMe
• NetMeeting, Face Mail
• Integration of text, voice, video

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Motivation (2)

• Face Mail
• Input
• Text - )
• Output
• Synthesized voice
• Animated character
• Multiple Info. Rep.!

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Motivation (3)

• Multiple levels of information representation

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Motivation (3)

• Multiple levels of information representation
• Some years ago browsers showed a low-resolution
  image first, then refined

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Motivation (3)

• Multiple levels of information representation
• Some years ago browsers showed a low-resolution
  image first, then refined
• In Virtual Environments distant objects are
  represented with lower Level Of Detail

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Motivation (3)

• Multiple levels of information representation
• Some years ago browsers showed a low-resolution
  image first, then refined
• In Virtual Environments distant objects are
  represented with lower Level Of Detail
• FaceMail you type text, output voice avatar

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Motivation (4)

• Multiple dimensions of information representation

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Motivation (4)

• Multiple dimensions of information representation
• Text easier to store, easier to search

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Motivation (4)

• Multiple dimensions of information representation
• Text easier to store, easier to search
• Sound hands-free, warmer interaction

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Motivation (4)

• Multiple dimensions of information representation
• Text easier to store, easier to search
• Sound hands-free, warmer interaction
• Text -gt Sound MS Whisper
• Sound -gt Text IBM Via-voice

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Motivation (6)

• Multiple dimensions of information representation
  (VSI)
• Company I used to work for.
• Product multi-modal representation of
  information for distance learning.
• Channels video sound text animations.

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Motivation (6)

• Multiple levels of information representation
  (VSI)
• Video Sound (engaging)

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Motivation (6)

• Multiple levels of information representation
  (VSI)
• Video Sound (engaging)
• Graphics Animations (concepts)

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Motivation (6)

• Multiple levels of information representation
  (VSI)
• Video Sound (engaging)
• Graphics Animations (concepts)
• Text (search)

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Motivation (6)

• After months of separate work in video, text,
  graphics

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Motivation (6)

• After months of separate work in video, text,
  graphics
• Integrated product was NOT SMOOTH!

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Motivation (6)

• After months of separate work in video, text,
  graphics
• Integrated product was NOT SMOOTH!
• Elaborate animations made video choke!

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Motivation (6)

• Integration find the appropriate space in a
  multi-dimensional space

Video Audio
Graphics
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Motivation (7)

• Multiple values in each dimension

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Motivation (7)

• Multiple values in each dimension
• Video resolution, frames per second

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Motivation (7)

• Multiple values in each dimension
• Video resolution, frames per second
• Sound sample resolution, sample rate

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Motivation (7)

• Multiple values in each dimension
• Video resolution, frames per second
• Sound sample resolution, sample rate
• In Networked Virtual Environments update rate

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Motivation (7)

• Multiple values in each dimension
• Video resolution, frames per second
• Sound sample resolution, sample rate
• In Networked Virtual Environments update rate
• Text ?

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Motivation (8)

• Higher values in each dimension

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Motivation (8)

• Higher values in each dimension
• More space to store

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Motivation (8)

• Higher values in each dimension
• More space to store
• More CPU cycles to process

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Motivation (8)

• Higher values in each dimension
• More space to store
• More CPU cycles to process
• More bandwidth to transmit

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Motivation (9)

• Computers in a Collaborative System
• Adapted from http//www.intel.com/intel/museum/25
  anniv/hof/moore.htm

generation
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Motivation (9)

• Computers in a Collaborative System
• Disparities among consecutive generations will
  grow larger in
• Processor speed
• Memory
• Bandwidth

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Motivation (9)

• How to cope with these disparities?

Slower computers
Faster computers
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Motivation - Summary

• Multiple modalities - multiple dimensions

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Motivation - Summary

• Multiple modalities - multiple dimensions
• Multiple qualities of information representation
  - multiple points

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Motivation - Summary

• Multiple modalities - multiple dimensions
• Multiple qualities of information representation
  - multiple points
• Performance - limits the valid hyper space

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Related Work (1)

• CVEs in Distance Education in Colombia

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Related Work (1)

• CVEs in Distance Education in Colombia
• Collaborative Virtual Environment as learning
  experience

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Related Work (1)

• CVEs in Distance Education in Colombia
• Collaborative Virtual Environment as learning
  experience
• AVALON (Carlos Correa)
• Multiple modalities
• Avatars, VRML worlds, OpenGL
• Voice multicast version of Speak freely
  (Francisco Cardona, now at Swiss Federal
  Institute of Technology (EPFL) )

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Related Work (1)

• CVEs in Distance Education in Colombia
• Used in a real learning environment
  (Environmental Issues class at Eafit University).
• Improvement in learning measured with the
  Teaching for Understanding model (Harvard)

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Related Work (2)

• Michael Macedonia - Ph.D. thesis (1995)
• Partition the space in hexagonal spaces
• Each hexagonal space is a multicast group
• Implemented in DIS (Distributed Interactive
  Simulation) and SIMNET (Simulator Networking)
• Read Thesis Statement

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Related Work (2)

• Thesis Statement
• Virtual environment software architectures can
  exploit wide area multicasting communications and
  entity relationships to partition the virtual
  world and enable the development of scalable
  distributed interactive simulations for military
  projects.
• (Michael Macedonia, 1995)

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Related Work (3)

• Michael Capps - Ph.D. thesis (2000)
• Have multiple representations for each object
• Define which representation to use based on
• Quality
• Importance
• Cost

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Our Thesis Statement

• It is possible to express global policies and
  individual users preferences in collaborative
  multimodal systems as a formal mathematical
  model. Solving this mathematical model, which
  includes performance measurements taken at each
  participating node, guarantees enforcement of the
  policies while allowing individual users to
  adjust their interaction with the system.

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Thesis Statement
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Assumptions

• Collaborative System
• Diverse degrees of computing power
• Nodes have limited computing power
• Shared information varying levels of fidelity in
  time/space dimensions

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Experimental Setup (1)

• Distributed visualization system
• Users share a Visualization Data Set
• Video of the other participants
• Telepointers
• One user can manipulate the object at a time

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Experimental Setup (2)

• User Interface

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Experimental Setup (3)

• Information Dimensions (variables)

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Experimental Setup (3)

• Information Dimensions (variables)
• Video

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Experimental Setup (3)

• Information Dimensions (variables)
• Video
• Object Movements

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Experimental Setup (3)

• Information Dimensions (variables)
• Video
• Object Movements
• Telepointer Movements

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Experimental Setup (3)

• Information Dimensions (variables)
• Video
• Object Movements
• Telepointer Movements
• Graphic complexity used locally for the
  visualized data set

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Experimental Setup (3)

• Information Dimensions (variables)
• Video
• Object Movements
• Telepointer Movements
• Graphic complexity used locally for the
  visualized data set
• Rendered object frames per second

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Experimental Setup (4)

• Independent variables
• Video
• Object Movements
• Telepointer Movements
• Graphic complexity used locally for the
  visualized data set
• Dependent variable
• Rendered object frames per second

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Experimental Setup (5)

• Mapping from independent to dependent variable

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Experimental Setup (5)

• Mapping from independent to dependent variable
• Performance Mapping

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Experimental Setup (5)

• Mapping from independent to dependent variable
• Performance Mapping
• Individual for each machine
• Determined before the collaborative session
  running a simulated session

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Experimental Setup (6)

• Architecture

Server
Client 1
Client 2
Client n
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Experimental Setup (6)

• Switchboard analogy

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Mathematical Model (1)

• Measurable way to express
• User preferences
• Minimum performance levels
• Maximum level of service that can be offered
• Server processing cycles
• Available bandwidth

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Mathematical Model (1)

• User preferences (i)
• Objective Function.

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Mathematical Model (1)

• Minimum performance levels (ii)
• V gt 1
• G gt 1
• T gt 5
• M gt 1
• F gt 2

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Mathematical Model (1)

• Maximum level of service (iii)
• Sum (Vi) lt 100
• Sum (Ti) lt 150
• Sum (Mi) lt 120
• Gi and Fi have only local impact

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Mathematical Model (1)

• Can be solved locally (at each machine)
• Individual Objective Function type (ii)
  restrictions
• Require a global solution
• Type (iii) restrictions Global Objective
  Function
• Possibility to solve the first part in parallel.

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Mathematical Model (2)

• Combining the three types of equations
• A LINEAR PROGRAMMING OPTIMIZATION PROBLEM

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Mathematical Model (2)

• Combining the three types of equations
• A LINEAR PROGRAMMING PROBLEM
• Each local solution
• Valid search space enforcing global policies
• Optimize local Objective Function
  allowing each user to adjust her interaction

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Mathematical Model (2)

• Global Solution
• Determine (and enforce) top limit on service
  handled by the server and the network

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Mathematical Model (3)

• Challenges
• In classic Linear Programming variables take
  continuous values.
• In our problem discrete values.
• Parallel processing of partial solutions
• Once a solution is found, if a condition changes,
  the next solution should be found without
  restarting the computation.

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Mathematical Model (3)

• Possible solutions
• Use integer programming
• Use piece-wise linear programming
• Non-linear optimization techniques

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Preliminary results (1)

• Server
• Accepts TCP connections for meta-information
• A predefined multicast group for each socket in
  the switchboard

TCP
Timer
UDP multicast
UDP unicast
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Preliminary results (2)

• Client
• Establishes a TCP connection for subscription
  messages
• Receives simulated updates from the server

TCP
SS
Timer
UDP (mcast)
UDP (unicast)
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Preliminary results(3)

• Computers
• Dragonfire Pentium II _at_ 350 MHz, 64MB main
  memory
• Morlak Pentium II _at_ 400 MHz, 256 MB main memory

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Preliminary results(3)

• 3D Models
• Guts 27,202 vertices
• Skull 1,210 vertices

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Preliminary results(4)

• Idle performance

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Preliminary results(5)

• Effect of video-events frequency

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Preliminary results(5)

• Effect of object-movement-events frequency

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Preliminary results(5)

• Note that video events have more impact on the
  local performance than object movement events.

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Preliminary results(6)

• Resulting performance mapping (Skull)

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Preliminary results(6)

• Resulting performance mapping (Guts)

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Future Work (1)

• Server
• Receive and cache (not queue) messages.

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Future Work (1)

• Server
• Receive and cache (not queue) messages.
• Client
• Implement frame grabbing
• Send updates

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Future Work (2)

• Math
• Local solution for type (i) and type (ii)
  equations
• Global solution involving type (iii) equations
• Choose solver
• Initially brute force (search through the
  finite search space)
• Linear programming?
• Non-linear programming?

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Conclusions (1)

• Mathematical model provides objective solution
  for two types of conflicting situations
• Diverse Computing power of the nodes
• Global policies and users preferences

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Conclusions (2)

• Assigning different multicast groups to the
  different values the variables can take make the
  solution scalable.

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Thank you!
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