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Editors contains: "Yang, Jun"

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  1. ; ; ; (, A Quarterly bulletin of the Computer Society of the IEEE Technical Committee on Data Engineering)
    Roy, Sudeepa; Yang, Jun (Ed.)
    Query Visualization (QV) is the problem of transforming a given query into a graphical representation that helps humans understand its meaning. This task is notably different from designing a Visual Query Language (VQL) that helps a user compose a query. This article discusses the principles of relational query visualization and its potential for simplifying user interactions with relational data. 
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    Accepted Manuscript Full Text Available
  2. ; ; ; (, A Quarterly bulletin of the Computer Society of the IEEE Technical Committee on Data Engineering)
    Wang, Haixun; Li, Chengkai; Yang, Jun (Ed.)
    In settings where an outcome, a decision, or a statement is made based on a single option among alternatives, it is popular to cherry-pick the data to generate an outcome that is supported by the cherry-picked data but not in general. In this paper, we use perturbation as a technique to design a support measure to detect, and resolve, cherry-picking across different contexts. In particular, to demonstrate the general scope of our proposal, we study cherry picking in two very different domains: (a) political statements based on trend-lines and (b) linear rankings. We also discuss sampling-based estimation as an effective and efficient approximation approach for detecting and resolving cherry-picking at scale. 
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    Accepted Manuscript Full Text Available