Trelliscope: A System for Detailed Visualization in the Deep Analysis of Large Complex Data

Abstract

Trelliscope emanates from the Trellis Display framework for visualization and the Divide and Recombine (D&R) approach to analyzing large complex data. In Trellis, the data are broken up into subsets, a visualization method is applied to each subset, and the display result is an array of panels, one per subset. This is a powerful framework for visualization of data, both small and large. In D&R, the data are broken up into subsets, and any analytic method from statistics and machine learning is applied to each subset independently. Then the outputs are recombined. This provides not only a powerful framework for analysis, but also feasible and practical computations using distributed computational facilities. It enables deep analysis of the data: study of both data summaries as well as the detailed data at their finest granularity.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2013
Accession Number
ADA596023

Entities

People

  • Jason McDermott
  • Karin Rodland
  • Kerstin Kleese-van Dam
  • Luke Gosink
  • Ryan Hafen
  • William S. Cleveland

Organizations

  • Pacific Northwest National Laboratory

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Computations
  • Computer Programming
  • Data Analysis
  • Data Mining
  • Data Sets
  • Data Visualization
  • Databases
  • Detectors
  • Engineering
  • High Level Languages
  • Information Science
  • Language
  • Machine Learning
  • Sampling
  • Systems Engineering
  • Visualizations

Fields of Study

  • Computer science

Readers

  • Computer Science/Computer Engineering/Data Science/Digital Signal Processing.
  • Distributed Systems and Data Platform Development
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks