research-article

Vizdom: interactive analytics through pen and touch

Authors:

Andrew Crotty,

Alex Galakatos,

Emanuel Zgraggen,

Carsten Binnig, and

Tim KraskaAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 8, Issue 12

Pages 2024 - 2027

https://doi.org/10.14778/2824032.2824127

Published: 01 August 2015 Publication History

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Abstract

Machine learning (ML) and advanced statistics are important tools for drawing insights from large datasets. However, these techniques often require human intervention to steer computation towards meaningful results. In this demo, we present Vizdom, a new system for interactive analytics through pen and touch. Vizdom's frontend allows users to visually compose complex workflows of ML and statistics operators on an interactive whiteboard, and the back-end leverages recent advances in workflow compilation techniques to run these computations at interactive speeds. Additionally, we are exploring approximation techniques for quickly visualizing partial results that incrementally refine over time. This demo will show Vizdom's capabilities by allowing users to interactively build complex analytics workflows using real-world datasets.

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Index Terms

Vizdom: interactive analytics through pen and touch
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification

Index terms have been assigned to the content through auto-classification.

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cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 8, Issue 12

Proceedings of the 41st International Conference on Very Large Data Bases, Kohala Coast, Hawaii

August 2015

728 pages

ISSN:2150-8097

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Published: 01 August 2015

Published in PVLDB Volume 8, Issue 12

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Li ZChockchowwat SFang HSahu RThakurdesai SPridaphatrakun KPark YBarcelo PSanchez-Pi NMeliou ASudarshan S(2024)Demonstration of ElasticNotebook: Migrating Live Computational Notebook StatesCompanion of the 2024 International Conference on Management of Data10.1145/3626246.3654752(540-543)Online publication date: 9-Jun-2024
https://dl.acm.org/doi/10.1145/3626246.3654752
Chang JDionysio CNargesian FBoehm MBarcelo PSanchez-Pi NMeliou ASudarshan S(2024)PLUTUS: Understanding Data Distribution Tailoring for Machine LearningCompanion of the 2024 International Conference on Management of Data10.1145/3626246.3654745(528-531)Online publication date: 9-Jun-2024
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Vaithilingam PGlassman EInala JWang C(2024)DynaVis: Dynamically Synthesized UI Widgets for Visualization EditingProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642639(1-17)Online publication date: 11-May-2024
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Li ZGor PPrabhu RYu HMao YPark Y(2023)ElasticNotebook: Enabling Live Migration for Computational NotebooksProceedings of the VLDB Endowment10.14778/3626292.362629617:2(119-133)Online publication date: 1-Oct-2023
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Li JSun YLei ZChen SAndrienko GAndrienko NChen W(2022)A hybrid prediction and search approach for flexible and efficient exploration of big dataJournal of Visualization10.1007/s12650-022-00887-y26:2(457-475)Online publication date: 6-Oct-2022
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Shang ZZgraggen EBuratti BEichmann PKarimeddiny NMeyer CRunnels WKraska T(2021)DavosProceedings of the VLDB Endowment10.14778/3476311.347637014:12(2893-2905)Online publication date: 28-Oct-2021
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