FlowSense: A natural language interface for visual data exploration within a dataflow system

Research output: Contribution to journalArticlepeer-review

Abstract

Dataflow visualization systems enable flexible visual data exploration by allowing the user to construct a dataflow diagram that composes query and visualization modules to specify system functionality. However learning dataflow diagram usage presents overhead that often discourages the user. In this work we design FlowSense, a natural language interface for dataflow visualization systems that utilizes state-of-The-Art natural language processing techniques to assist dataflow diagram construction. FlowSense employs a semantic parser with special utterance tagging and special utterance placeholders to generalize to different datasets and dataflow diagrams. It explicitly presents recognized dataset and diagram special utterances to the user for dataflow context awareness. With FlowSense the user can expand and adjust dataflow diagrams more conveniently via plain English. We apply FlowSense to the VisFlow subset-flow visualization system to enhance its usability. We evaluate FlowSense by one case study with domain experts on a real-world data analysis problem and a formal user study.

Original languageEnglish (US)
Article number8807265
Pages (from-to)1-11
Number of pages11
JournalIEEE Transactions on Visualization and Computer Graphics
Volume26
Issue number1
DOIs
StatePublished - Jan 2020

Keywords

  • Dataflow visualization system
  • Natural language interface
  • Visual data exploration

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

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