Towards visualization recommendation systems

Manasi Vartak, Silu Huang, Tarique Siddiqui, Samuel Madden, Aditya Parameswaran

Research output: Contribution to journalArticle

Abstract

Data visualization is often used as the first step while performing a variety of analytical tasks. With the advent of large, high-dimensional datasets and significant interest in data science, there is a need for tools that can support rapid visual analysis. In this paper we describe our vision for a new class of visualization systems, namely visualization recommendation systems, that can automatically identify and interactively recommend visualizations relevant to an analytical task. We detail the key requirements and design considerations for a visualization recommendation system. We also identify a number of challenges in realizing this vision and describe some approaches to address them.

Original languageEnglish (US)
Pages (from-to)34-39
Number of pages6
JournalSIGMOD Record
Volume45
Issue number4
DOIs
StatePublished - Dec 2016

ASJC Scopus subject areas

  • Software
  • Information Systems

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    Vartak, M., Huang, S., Siddiqui, T., Madden, S., & Parameswaran, A. (2016). Towards visualization recommendation systems. SIGMOD Record, 45(4), 34-39. https://doi.org/10.1145/3092931.3092937