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Optimizing Dataflow Systems for Scalable Interactive Visualization

Authors:

Hyekang Kevin Joo,

Sai Yerramreddy,

Dominik Moritz,

Leilani BattleAuthors Info & Claims

Proceedings of the ACM on Management of Data, Volume 2, Issue 1

Article No.: 21, Pages 1 - 25

https://doi.org/10.1145/3639276

Published: 26 March 2024 Publication History

Abstract

Supporting the interactive exploration of large datasets is a popular and challenging use case for data management systems. Traditionally, the interface and the back-end system are built and optimized separately, and interface design and system optimization require different skill sets that are difficult for one person to master. To enable analysts to focus on visualization design, we contribute VegaPlus, a system that automatically optimizes interactive dashboards to support large datasets. To achieve this, VegaPlus leverages two core ideas. First, we introduce an optimizer that can reason about execution plans in Vega, a back-end DBMS, or a mix of both environments. The optimizer also considers how user interactions may alter execution plan performance, and can partially or fully rewrite the plans when needed. Through a series of benchmark experiments on seven different dashboard designs, our results show that VegaPlus provides superior performance and versatility compared to standard dashboard optimization techniques.

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Cited By

Ferreira LMoreira GHosseini MLage MFerreira NMiranda F(2024)Assessing the landscape of toolkits, frameworks, and authoring tools for urban visual analytics systemsComputers and Graphics10.1016/j.cag.2024.104013123:COnline publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1016/j.cag.2024.104013

Index Terms

Optimizing Dataflow Systems for Scalable Interactive Visualization
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interactive systems and tools
  2. Visualization
    1. Visualization systems and tools
2. Information systems
  1. Data management systems

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cover image Proceedings of the ACM on Management of Data

Proceedings of the ACM on Management of Data Volume 2, Issue 1

SIGMOD

February 2024

1874 pages

EISSN:2836-6573

DOI:10.1145/3654807

Editor:
Divyakant Agrawal
UC Santa Barbara, United States

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Copyright © 2024 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

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Published: 26 March 2024

Published in PACMMOD Volume 2, Issue 1

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Ferreira LMoreira GHosseini MLage MFerreira NMiranda F(2024)Assessing the landscape of toolkits, frameworks, and authoring tools for urban visual analytics systemsComputers and Graphics10.1016/j.cag.2024.104013123:COnline publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1016/j.cag.2024.104013

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