Explanatory Lineage

نویسندگان

  • Eugene Wu
  • Samuel Madden
چکیده

As data analytics becomes mainstream, and the complexity of the underlying data and computation grows, end-users will increasingly rely on visualizations to present simplified statistics that summarizes interesting properties in the data. It is even more important to provide tools that help analysts understand the underlying reasons when they see anomalous results. We envision adding a new explanatory dimension into visualization libraries and creation tools that automatically give end-users the capability to mine and understand the reasons for outliers and trends that they see in the visualizations. In this paper, we propose an initial problem formulation targeted towards business dashboard applications, and propose a set of modifications to existing declarative visualization libraries that enables this ability in existing visualizations with minimal changes by the developer.

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تاریخ انتشار 2012