research-article

DataVizard: Recommending Visual Presentations for Structured Data

Authors:

Rema Ananthanarayanan,

Pranay K. Lohia,

Srikanta BedathurAuthors Info & Claims

WebDB'18: Proceedings of the 21st International Workshop on the Web and Databases

Article No.: 3, Pages 1 - 6

https://doi.org/10.1145/3201463.3201465

Published: 10 June 2018 Publication History

Abstract

Selecting the appropriate visual presentation of the data such that it not only preserves the semantics but also provides an intuitive summary of the data is an important, often the final step of data analytics. Unfortunately, this is also a step involving significant human effort starting from selection of groups of columns in the structured results from analytics stages, to the selection of right visualization by experimenting with various alternatives. In this paper, we describe our DataVizard system aimed at reducing this overhead by automatically recommending the most appropriate visual presentation for the structured result. Specifically, we consider the following two scenarios: first, when one needs to visualize the results of a structured query such as SQL; and the second, when one has acquired a data table with an associated short description (e.g., tables from the Web). Using a corpus of real-world database queries (and their results) and a number of statistical tables crawled from the Web, we show that DataVizard is capable of recommending visual presentations with high accuracy.

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Cited By

Wang JLi XLi CPeng DWang AGu YLai XZhang HXu XDong XLin ZZhou JLiu XChen W(2024)AVA: An automated and AI-driven intelligent visual analytics frameworkVisual Informatics10.1016/j.visinf.2024.06.0028:2(106-114)Online publication date: Jun-2024
https://doi.org/10.1016/j.visinf.2024.06.002
Vij RRaj RSinghal MTanwar MBedathur S(2022)VizAI : Selecting Accurate Visualizations of Numerical DataProceedings of the 5th Joint International Conference on Data Science & Management of Data (9th ACM IKDD CODS and 27th COMAD)10.1145/3493700.3493717(28-36)Online publication date: 8-Jan-2022
https://dl.acm.org/doi/10.1145/3493700.3493717
Wu AWang YShu XMoritz DCui WZhang HZhang DQu H(2022)AI4VIS: Survey on Artificial Intelligence Approaches for Data VisualizationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2021.309900228:12(5049-5070)Online publication date: 1-Dec-2022
https://doi.org/10.1109/TVCG.2021.3099002
Show More Cited By

Index Terms

DataVizard: Recommending Visual Presentations for Structured Data
1. Human-centered computing
  1. Visualization
    1. Visualization application domains
      1. Information visualization

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cover image ACM Conferences

WebDB'18: Proceedings of the 21st International Workshop on the Web and Databases

June 2018

32 pages

ISBN:9781450356480

DOI:10.1145/3201463

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 10 June 2018

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SIGMOD/PODS '18

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SIGMOD

SIGMOD/PODS '18: International Conference on Management of Data

June 10, 2018

TX, Houston, USA

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WebDB'18 Paper Acceptance Rate 5 of 19 submissions, 26%;

Overall Acceptance Rate 30 of 100 submissions, 30%

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Cited By

Wang JLi XLi CPeng DWang AGu YLai XZhang HXu XDong XLin ZZhou JLiu XChen W(2024)AVA: An automated and AI-driven intelligent visual analytics frameworkVisual Informatics10.1016/j.visinf.2024.06.0028:2(106-114)Online publication date: Jun-2024
https://doi.org/10.1016/j.visinf.2024.06.002
Vij RRaj RSinghal MTanwar MBedathur S(2022)VizAI : Selecting Accurate Visualizations of Numerical DataProceedings of the 5th Joint International Conference on Data Science & Management of Data (9th ACM IKDD CODS and 27th COMAD)10.1145/3493700.3493717(28-36)Online publication date: 8-Jan-2022
https://dl.acm.org/doi/10.1145/3493700.3493717
Wu AWang YShu XMoritz DCui WZhang HZhang DQu H(2022)AI4VIS: Survey on Artificial Intelligence Approaches for Data VisualizationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2021.309900228:12(5049-5070)Online publication date: 1-Dec-2022
https://doi.org/10.1109/TVCG.2021.3099002
Bautista YAlo RWang N(2020)Deep Learning, Cloud Computing for Credit/Debit Industry Analysis of Consumer Behavior2020 7th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/2020 6th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom)10.1109/CSCloud-EdgeCom49738.2020.00010(1-7)Online publication date: Aug-2020
https://doi.org/10.1109/CSCloud-EdgeCom49738.2020.00010
Golfarelli MRizzi S(2019)A model-driven approach to automate data visualization in big data analyticsInformation Visualization10.1177/147387161985893319:1(24-47)Online publication date: 24-Jul-2019
https://doi.org/10.1177/1473871619858933

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