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A Framework for Digital Object Self-Preservation

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Unsupervised Creation of Small World Networks for the Preservation of Digital Objects Charles L. Cartledge Michael L. Nelson Old Dominion University – PowerPoint PPT presentation

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Title: A Framework for Digital Object Self-Preservation


1
Unsupervised Creation of Small World Networks for
the Preservation of Digital Objects
Charles L. Cartledge Michael L. Nelson Old
Dominion University Department of Computer
Science Norfolk, Virginia
2
Order of Presentation
  • Technology enablers
  • Constraints
  • Simple rules for Complex Behavior
  • Simulation approach
  • Simulation results
  • Future work

3
Motivation
4
Technology Enablers
Cost data http//www.archivebuilders.com/whitepap
ers/22011p.pdf
5
Constraints

6
Reynoldss Rules for Flocking
  • Collision Avoidance avoid collisions with nearby
    flock mates
  • Velocity Matching attempt to match velocity with
    nearby flock mates
  • Flock Centering attempt to stay close to nearby
    flock mates

Images and rules http//www.red3d.com/cwr/boids/
Doctoral Consortium
6
7
Types of Graphs
(Each graph has 20 vertices and 40 edges.)
8
Desirable Graph Properties
9
Unsupervised Small World Graph Creation
  • gamma 0.0
  • alpha 0.99
  • gamma 0.7
  • alpha 0.99
  • 0.2 lt beta lt0.66
  • gamma lt 0.6

CC is shown as dark lines L is shown as light
lines
10
Phases/Activities
11
Creation
Any DO
12
Wandering
A
B
D
C
13
Connecting
A
B
D
C
14
Flocking
A
A
A
A
15
Typical Simulation Parameters
  • alpha 0.5
  • beta 0.6
  • gamma 0.1
  • Number of DOs 1000
  • Number of hosts 1000
  • Min number desired replicas 3
  • Max number desired replicas 10
  • Max number of replicas per host 20

16
Simulation Results and Analysis
17
Future work
  • Test the autonomous graphs for resilience to
    error and attack
  • Test what happens when a graph becomes
    disconnected
  • Test what happens when a disconnected graph
    becomes re-connected

18
Conclusions
  • We have shown that Digital Objects can
    autonomously create small world graphs based on
    locally gleaned data
  • These graphs can be used for long term
    preservation
  • We intend to study these graphs focusing on their
    tolerance to isolated and widespread failures

19
And that concludes my presentation.
20
Backup Information
  • Equations for Average Path Length and Clustering
    Coefficients
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