Employing a Parametric Model for Analytic Provenance
Employing a Parametric Model for Analytic Provenance
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采用参数模型进行分析来源
DOI：
10.1145/2591510
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
C. Shaw
中科院分区：
文献类型：
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作者：
Victor Y. Chen;Cheryl Z. Qian;R. Woodbury;J. Dill;C. Shaw
我们介绍了一种基于传播的参数符号模型方法来支持分析出处。这种方法结合了一个脚本语言来捕获和编码的分析过程和一个参数控制的符号模型来表示和重用的逻辑分析过程。我们的方法首先出现在一个名为CZSaw的可视化分析系统中。使用一个脚本来捕捉分析师的互动在一个有意义的系统动作级别允许创建一个参数化控制的符号模型的形式有向无环图（DAG）。使用DAG允许传播更改。图形节点对应于CZSaw脚本中的变量，这些变量是从用户交互生成的结果（数据和数据可视化）。用户与表示实体或关系的变量交互以创建下一步的结果。图边表示节点之间的依赖关系。对变量的任何更改都会触发传播机制来更新下游的因变量，进而更新数据视图以反映更改。分析人员可以通过为图中的节点分配新值来重用分析过程的各个部分。我们通过解决三个IEEE VAST挑战赛竞赛问题（来自IEEE VAST 2008，2009和2010）来评估这种符号模型方法。在这些挑战中，分析师首先创建一个符号模型来探索，理解，分析和解决特定的子问题，然后通过依赖图传播机制重用模型来解决类似的子问题。通过脚本和模型，CZSaw通过捕获、编码和重用分析过程来支持分析出处。分析师可以用CZSaw脚本回忆分析过程的时间顺序状态，并可以用符号模型解释分析的基本原理。
We introduce a propagation-based parametric symbolic model approach to supporting 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 logic of the analysis process. Our approach first appeared in a visual analytics system called CZSaw. Using a script to capture the analyst’s interactions at a meaningful system action level allows the creation of a parametrically controlled symbolic model in the form of a Directed Acyclic Graph (DAG). Using the DAG allows propagating changes. Graph nodes correspond to variables in CZSaw scripts, which are results (data and data visualizations) generated from user interactions. The user interacts with variables representing entities or relations to create the next step’s results. Graph edges represent dependency relationships among nodes. Any change to a variable triggers the propagation mechanism to update downstream dependent variables and in turn updates data views to reflect the change. The analyst can reuse parts of the analysis process by assigning new values to a node in the graph. We evaluated this symbolic model approach by solving three IEEE VAST Challenge contest problems (from IEEE VAST 2008, 2009, and 2010). In each of these challenges, the analyst first created a symbolic model to explore, understand, analyze, and solve a particular subproblem and then reused the model via its dependency graph propagation mechanism to solve similar subproblems. With the script and model, CZSaw supports the analytic provenance by capturing, encoding, and reusing the analysis process. The analyst can recall the chronological states of the analysis process with the CZSaw script and may interpret the underlying rationale of the analysis with the symbolic model.