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SumUp: Statistical Visual Query of Multivariate Data with Parallel-Coordinate Geometry

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  • First Online:
Cooperative Design, Visualization, and Engineering (CDVE 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9929))

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Abstract

One of the most noticeable issues of parallel coordinate visualization is how to quantitatively analyze density caused by polyline growth in a limited space on axes. The existing visualization tools only support the comparison among single dimensions and single ranges of polylines, which could face limitation in cases of complicated analytics. This paper proposes a new visual-query technique, named SumUp, for statistical analysis of multiple attributes of dimensions and multiple ranges of polylines. The methodology of SumUp is primarily based on developing dynamic queries using brushing operations to deliver summary stacked bars adaptive with parallel coordinates. Users can easily observe quantitative information from data patterns and compare multiple attributes over the density of polylines in the parallel coordinate visualization. Early experiments show that our proposed technique could potentially enhance the manipulation on parallel coordinates, showing by a typical case study.

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Author information

Authors and Affiliations

  1. University of Technology, Sydney, Australia

    Phi Giang Pham & Mao Lin Huang

  2. Tianjin University, Tianjin, China

    Mao Lin Huang

  3. University of Western Sydney, Sydney, Australia

    Quang Vinh Nguyen

Authors
  1. Phi Giang Pham
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  2. Mao Lin Huang
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  3. Quang Vinh Nguyen
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Corresponding author

Correspondence to Mao Lin Huang .

Editor information

Editors and Affiliations

  1. University of the Balearic Islands, Palma, Mallorca, Spain

    Yuhua Luo

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© 2016 Springer International Publishing AG

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Pham, P.G., Huang, M.L., Nguyen, Q.V. (2016). SumUp: Statistical Visual Query of Multivariate Data with Parallel-Coordinate Geometry. In: Luo, Y. (eds) Cooperative Design, Visualization, and Engineering. CDVE 2016. Lecture Notes in Computer Science(), vol 9929. Springer, Cham. https://doi.org/10.1007/978-3-319-46771-9_51

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  • DOI: https://doi.org/10.1007/978-3-319-46771-9_51

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46770-2

  • Online ISBN: 978-3-319-46771-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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