SEEDB: Efficient data-driven visualization recommendations to support visual analytics

Manasi Vartak, Sajjadur Rahman, Samuel Madden, Aditya Parameswaran, Neoklis Polyzotis

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Data analysts often build visualizations as the first step in their analytical workflow. However, when working with high-dimensional datasets, identifying visualizations that show relevant or desired trends in data can be laborious. We propose SEEDB, a visualization recommendation engine to facilitate fast visual analysis: given a subset of data to be studied, SEEDB intelligently explores the space of visualizations, evaluates promising visualizations for trends, and recommends those it deems most "useful" or "interesting". The two major obstacles in recommending interesting visualizations are (a) scale: evaluating a large number of candidate visualizations while responding within interactive time scales, and (b) utility: identifying an appropriate metric for assessing interestingness of visualizations. For the former, SEEDB introduces pruning optimizations to quickly identify high-utility visualizations and sharing optimizations to maximize sharing of computation across visualizations. For the latter, as a first step, we adopt a deviationbased metric for visualization utility, while indicating how we may be able to generalize it to other factors influencing utility. We implement SEEDB as a middleware layer that can run on top of any DBMS. Our experiments show that our framework can identify interesting visualizations with high accuracy. Our optimizations lead to multiple orders of magnitude speedup on relational row and column stores and provide recommendations at interactive time scales. Finally, we demonstrate via a user study the effectiveness of our deviation-based utility metric and the value of recommendations in supporting visual analytics.

Original languageEnglish (US)
Title of host publicationProceedings of the VLDB Endowment
PublisherAssociation for Computing Machinery
Pages2182-2193
Number of pages12
Edition13
DOIs
StatePublished - 2015
Event3rd Workshop on Spatio-Temporal Database Management, STDBM 2006, Co-located with the 32nd International Conference on Very Large Data Bases, VLDB 2006 - Seoul, Korea, Republic of
Duration: Sep 11 2006Sep 11 2006

Publication series

NameProceedings of the VLDB Endowment
Number13
Volume8
ISSN (Electronic)2150-8097

Other

Other3rd Workshop on Spatio-Temporal Database Management, STDBM 2006, Co-located with the 32nd International Conference on Very Large Data Bases, VLDB 2006
CountryKorea, Republic of
CitySeoul
Period9/11/069/11/06

ASJC Scopus subject areas

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

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