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SamS-Vis: A Tool to Visualize Summary View Using Sampled Data

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Human-Computer Interaction – INTERACT 2023 (INTERACT 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14145))

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Abstract

Many recent visual analytics tools use exploratory model analysis workflow to enable users exploring set of potential machine/deep learning models. As part of the workflow, these tools provide summary view of underlying dataset to enable the users to better understand trends in their data. Due to the iterative nature of such workflows, users may need to go back to data exploration phase multiple times. In order to save time and resources at data pre-processing and visualization time, we propose to use sampled data rather than complete dataset for showing trends in data summary views. As a proof-of-concept, we built a visualization tool, called SamS-Vis, that uses five sampling techniques to collect sampled data and then shows the summary views using histogram line-charts. It enables the users to see the whole data summary view of the selected field(s) using histogram bar-chart based on demand.

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Author information

Authors and Affiliations

  1. Department of Computer Science, San Francisco State University, San Francisco, USA

    Shah Rukh Humayoun

  2. University of Kaiserslautern, Kaiserslautern, Germany

    Salman Zaidi

  3. Intel Labs, Intel Corporation, Santa Clara, USA

    Ragaad AlTarawneh

Authors
  1. Shah Rukh Humayoun
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  2. Salman Zaidi
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  3. Ragaad AlTarawneh
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Corresponding author

Correspondence to Shah Rukh Humayoun .

Editor information

Editors and Affiliations

  1. University of West London, London, UK

    José Abdelnour Nocera

  2. Reykjavik University, Reykjavik, Iceland

    Marta Kristín Lárusdóttir

  3. University of York, York, UK

    Helen Petrie

  4. University of Bari Aldo Moro, Bari, Italy

    Antonio Piccinno

  5. Université Côte d’Azur, Sophia Antipolis Cedex, France

    Marco Winckler

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Humayoun, S.R., Zaidi, S., AlTarawneh, R. (2023). SamS-Vis: A Tool to Visualize Summary View Using Sampled Data. In: Abdelnour Nocera, J., Kristín Lárusdóttir, M., Petrie, H., Piccinno, A., Winckler, M. (eds) Human-Computer Interaction – INTERACT 2023. INTERACT 2023. Lecture Notes in Computer Science, vol 14145. Springer, Cham. https://doi.org/10.1007/978-3-031-42293-5_72

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  • DOI: https://doi.org/10.1007/978-3-031-42293-5_72

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-42292-8

  • Online ISBN: 978-3-031-42293-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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