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Composite rectilinear deformation for stretch and squish navigation.

James Slack1, Tamara Munzner

  • 1Department of Computer Science, University of British Columbia. jslack@cs.ubc.ca

IEEE Transactions on Visualization and Computer Graphics
|November 4, 2006
PubMed
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We developed a new scalable algorithm for successive rectilinear deformations, enabling efficient navigation of large datasets. This stretch and squish navigation method handles millions of regions with sub-millisecond performance.

Area of Science:

  • Computer Science
  • Data Visualization
  • Algorithm Design

Background:

  • Previous navigation systems for large datasets were limited in scope.
  • Existing methods for stretch and squish navigation primarily focused on rendering or specific applications, lacking a general algorithmic solution.

Purpose of the Study:

  • To present the first scalable algorithm for composing successive rectilinear deformations.
  • To enable efficient navigation of large datasets with millions of deformable regions using stretch and squish techniques.

Main Methods:

  • Developed a novel algorithm with O(klogn) time complexity for handling deformations.
  • Designed an algorithm that avoids computations linear in the total number of regions (n).
  • Implemented the algorithm for navigating large-scale data structures like trees and gene sequences.

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Main Results:

  • The algorithm efficiently supports the composition of successive rectilinear deformations.
  • Achieved sub-millisecond navigation times for datasets with millions of items.
  • Demonstrated flexibility for applications that can arrange data on a generic grid.

Conclusions:

  • The presented algorithm offers a scalable solution for stretch and squish navigation on large datasets.
  • This work overcomes limitations of prior systems by providing a general and efficient method for deformation composition.
  • The algorithm facilitates interactive exploration of complex data structures like gene sequences and hierarchical data.