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QAGView: Interactively Summarizing High-Valued Aggregate Query Answers.
Wen, Yuhao; Zhu, Xiaodan; Roy, Sudeepa; Yang, Jun.
Afiliação
  • Wen Y; Department of Computer Science, Duke University Durham, North Carolina.
  • Zhu X; Department of Computer Science, Duke University Durham, North Carolina.
  • Roy S; Department of Computer Science, Duke University Durham, North Carolina.
  • Yang J; Department of Computer Science, Duke University Durham, North Carolina.
Proc ACM SIGMOD Int Conf Manag Data ; 2018: 1709-1712, 2018 Jun.
Article em En | MEDLINE | ID: mdl-31447495
ABSTRACT
Methods for summarizing and diversifying query results have drawn significant attention recently, because they help present query results with lots of tuples to users in more informative ways. We present QAGView (Quick AGgregate View), which provides a holistic overview of high-valued aggregate query answers to the user in the form of summaries (showing high-level properties that emerge from subsets of answers) with coverage guarantee (for a user-specified number of top-valued answers) that is both diverse (avoiding overlapping or similar summaries) and relevant (focusing on high-valued aggregate answers). QAGView allows users to view the high-level summaries as clusters, and to expand individual clusters for their constituent result tuples. Users can fine-tune the behavior of QAGView by specifying a number of parameters according their preference. To help users choose appropriate parameters interactively, QAGView employ a suite of optimizations that enable quick preview of how the quality of the summaries changes over wide ranges of parameter settings, as well as real-time visualization of how the summaries evolve in response to parameter updates.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2018 Tipo de documento: Article