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Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Published on: February 25, 2013

An extensible framework for provenance in human terrain visual analytics.

Rick Walker1, Aiden Slingsby, Jason Dykes

  • 1Middlesex University.

IEEE Transactions on Visualization and Computer Graphics
|September 21, 2013
PubMed
Summary
This summary is machine-generated.

We developed a framework for Human Terrain Analysis (HTA) to track data exploration and provenance. This system, ProveML, uses analytic bookmarks to link interpretations back to live data, aiding intelligence analysis.

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Area of Science:

  • Computer Science
  • Information Science
  • Intelligence Studies

Background:

  • Human Terrain Analysis (HTA) requires robust methods for data exploration and provenance tracking.
  • Existing frameworks may not adequately support the complex analytical processes and interpretive nature of HTA data.

Purpose of the Study:

  • To design and demonstrate an extensible framework for data exploration and provenance in Human Terrain Analysis (HTA).
  • To develop a system that supports the recording, browsing, and recalling of analytical provenance and processes.
  • To validate the framework's utility with intelligence community end-users.

Main Methods:

  • Collaborative requirements extraction with defense analysts.
  • Modeling data features as facets.
  • Developing ProveML, an XML-based extension of the Open Provenance Model.
  • Iterative prototyping of a Human Terrain Visual Analytics (HTVA) system.
  • Utilizing analytic bookmarks within ProveML for provenance recall.

Main Results:

  • A functional prototype system for Human Terrain Visual Analytics (HTVA) was developed.
  • ProveML effectively records provenance of data, analytical processes, and interpretations.
  • Analytic bookmarks in ProveML enable narrative construction linking back to live data.
  • The framework demonstrated its ability to meet intelligence community requirements through user validation.

Conclusions:

  • The developed framework provides a structured and extensible solution for HTA data exploration and provenance.
  • Collaborative development and iterative prototyping are effective in meeting intelligence community needs.
  • The ProveML system enhances analytical transparency and traceability within HTA.