SEEDB: Automatically generating query visualizations

Manasi Vartak, Samuel Madden, Aditya Parameswaran, Neoklis Polyzotis

Research output: Contribution to journalArticlepeer-review

Abstract

Data analysts operating on large volumes of data often rely on visualizations to interpret the results of queries. However, finding the right visualization for a query is a laborious and time-consuming task. We demonstrate SEEDB, a system that partially automates this task: given a query, SEEDB explores the space of all possible visualizations, and automatically identifies and recommends to the analyst those visualizations it finds to be most "interesting" or "useful". In our demonstration, conference attendees will see SEEDB in action for a variety of queries on multiple real-world datasets.

Original languageEnglish (US)
Pages (from-to)1581-1584
Number of pages4
JournalProceedings of the VLDB Endowment
Volume7
Issue number13
DOIs
StatePublished - 2014

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computer Science(all)

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