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Designing and Evaluating Multimodal Interactions for Facilitating Visual Analysis With Dashboards.

Imran Chowdhury1, Abdul Moeid1, Enamul Hoque2

  • 1Department of Computer Science and EngineeringChittagong University of Engineering and Technology Chittagong 4349 Bangladesh.

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Summary
This summary is machine-generated.

This study introduces the Multimodal Interactions System for Visual Analysis (MIVA), combining natural language and direct manipulation for data exploration. MIVA enhances visual analysis flow across multiple coordinated visualizations on a dashboard.

Keywords:
Direct manipulationmultimodal interactionmultiple-coordinated viewsnatural language interface

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Area of Science:

  • Human-Computer Interaction
  • Data Visualization
  • Information Retrieval

Background:

  • Traditional visual data analysis relies on mouse/touch interactions, which can be limiting for complex tasks.
  • Natural language interaction offers a more intuitive way to pose complex analytical questions.
  • Combining modalities can leverage the strengths of each for improved data exploration.

Purpose of the Study:

  • To investigate the synergistic combination of natural language and direct manipulation for visual data analysis.
  • To develop and evaluate a novel system, MIVA, supporting multimodal interactions with dashboards.
  • To enable fluid, iterative exploration and refinement of data in multi-view dashboards.

Main Methods:

  • Developed the Multimodal Interactions System for Visual Analysis (MIVA).
  • Integrated natural language (speech) and direct manipulation (mouse/touch) input modalities.
  • Incorporated a finite-state model to maintain context from past interactions.
  • Enabled multimodal interaction with multiple coordinated visualizations on a dashboard.

Main Results:

  • MIVA was tested on various dashboards, including a COVID-19 dashboard.
  • A user study with twenty participants evaluated the system's effectiveness.
  • Results indicated that MIVA enhances the flow of visual analysis.
  • The system facilitates fluid, iterative exploration and refinement of data.

Conclusions:

  • MIVA successfully combines natural language and direct manipulation for enhanced visual data analysis.
  • The system's ability to interact with multiple coordinated visualizations is a key innovation.
  • MIVA improves the user experience by enabling more fluid and efficient data exploration.