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Outline

Title
Abstract
Introduction
Improvements Upon Previous Research
Interaction and Data Flow Among Czsaw Components
Use Cases
Use Case A: 2008 Mini Challenge MC3
Use Case B: 2009 Mini Challenge MC2
Use Case C: 2010 Mini Challenge MC1
Conclusions
References
All Topics
History

Employing a Parametric Model for Analytic Provenance

Profile image of Chris D ShawChris D Shaw

2014, ACM Transactions on Interactive Intelligent Systems

https://doi.org/10.1145/0000000.0000000
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34 pages

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Abstract

We introduce a propagation-based parametric symbolic model approach to support analytic provenance. This approach combines a script language to capture and encode the analytic process and a parametrically controlled symbolic model to represent and reuse the analytic process. Our approach first appeared in a visual analytics system called CZSaw. Capturing the analyst's interactions at a meaningful system action level as CZSaw scripts creates a parametrically controlled symbolic model in the form of a directed acyclic graph capable of propagating changes. Nodes in the graph (variables in CZSaw scripts) are results (data and data visualizations) generated from user interactions. Graph edges represent dependency relationships among results. The user interacts with the variables representing entities or relations to generate the next step's results. Any change to a variable triggers the propagation mechanism to update downstream variables and in turn update data views to reflect the change. The analyst can reuse parts of the analysis process by assigning new values to a node in the middle of the graph. We evaluated this symbolic model approach through solving three IEEE VAST "Challenge" contest problems [IEEE VAST 2008; 2009; 2010]. In each Challenge the analyst first created a symbolic model to help explore, understand, analyze, and solve a particular sub-problem, and later reused the model via its dependency graph propagation mechanism to solve similar sub-problems.

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