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The Impact of Streaming Data on Sensemaking with Mixed-Initiative Visual Analytics

  • Nick Cramer
  • Grant Nakamura
  • Alex EndertEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10284)

Abstract

Visual data analysis helps people gain insights into data via interactive visualizations. People generate and test hypotheses and questions about data in context of the domain. This process can generally be referred to as sensemaking. Much of the work on studying sensemaking (and creating visual analytic techniques in support of it) has been focused on static datasets. However, how do the cognitive processes of sensemaking change when data are changing? Further, what implication for design does this create for mixed-initiative visual analytics systems? This paper presents the results of a user study analyzing the impact of streaming data on sensemaking. To perform this study, we developed a mixed-initiative visual analytic prototype, the Streaming Canvas, that affords the analysis of streaming text data. We compare the sensemaking process of people using this tool for a static and streaming dataset. We present the results of this study and discuss the implications on future visual analytic systems that combine machine learning and interactive visualization to help people make sense of streaming data.

Keywords

Sensemaking Streaming data Visual analytics 

Notes

Acknowledgments

The research described in this paper is part of the Analysis In Motion Initiative and the Signature Discovery Initiative at Pacific Northwest National Laboratory. It was conducted under the Laboratory Directed Research and Development Program at PNNL, a multiprogram national laboratory operated by Battelle for the U.S. Department of Energy.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Pacific Northwest National LaboratoriesRichlandUSA
  2. 2.School of Interactive ComputingGeorgia Institute of TechnologyAtlantaUSA

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