short-paper

Effective Big Data Visualization

Authors:

Si FeiAuthors Info & Claims

IDEAS '17: Proceedings of the 21st International Database Engineering & Applications Symposium

Pages 298 - 303

https://doi.org/10.1145/3105831.3105857

Published: 12 July 2017 Publication History

Abstract

In the last several years, big data analytics has found an increasing role in our everyday lives. Data visualization has long been accepted as an integral part of data analytics. However, data visualization systems are not equipped to handle the complexities typically found in big data. Our work examines effective ways of visualizing big data, while also realizing that most visualization processes are interactive. During an interactive visualization session, an analyst issues several visualization requests, each of which builds on prior visualizations. In our approach, we integrate a distributed data processing system that can effectively process big data with a visualization system that can provide effective interactive visualization but for smaller amounts of data. The analyst's current request is used to infer contextual information about the analyst such as their expertise and tolerance for delay. This information is used to carefully determine additional data that can be sent to the visualization system for decreasing the response time for future requests, thus providing a better experience for the analyst and increasing their productivity.

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Cited By

Pulikottil TEstrada-Jimenez LAbadía JCarrera-Rivera ATorayev ARehman HMo FNikghadam-Hojjati SBarata J(2023)Big Data Life Cycle in Shop-Floor–Trends and ChallengesIEEE Access10.1109/ACCESS.2023.325328611(30008-30026)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3253286
Yang CXu XNunes BSiqueira S(2023)Bubbles bursting: Investigating and measuring the personalisation of social media searchesTelematics and Informatics10.1016/j.tele.2023.10199982(101999)Online publication date: Aug-2023
https://doi.org/10.1016/j.tele.2023.101999
Mohammed LAlHabshy AElDahshan K(2022)Big Data Visualization: A Survey2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)10.1109/HORA55278.2022.9799819(1-12)Online publication date: 9-Jun-2022
https://doi.org/10.1109/HORA55278.2022.9799819
Show More Cited By

Index Terms

Effective Big Data Visualization
1. Human-centered computing
  1. Visualization
    1. Visualization application domains
      1. Visual analytics
2. Information systems
  1. Information systems applications
    1. Decision support systems
      1. Data analytics

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Information & Contributors

Information

Published In

cover image ACM Other conferences

IDEAS '17: Proceedings of the 21st International Database Engineering & Applications Symposium

July 2017

338 pages

ISBN:9781450352208

DOI:10.1145/3105831

Editors:
Bipin C. Desai
Concordia University
,
Jun Hong
UWE, Bristol
,
Richard McClatchey
UWE, Bristol
,
General Chair:
Bipin C. Desai
Concordia University
,
Program Chair:
Jun Hong
UWE, Bristol

Copyright © 2017 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 ACM 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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Univ of the West of England: University of the West of England
BytePress
Concordia University: Concordia University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 July 2017

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IDEAS 2017

IDEAS 2017: 21st International Database Engineering & Applications Symposium

July 12 - 14, 2017

Bristol, United Kingdom

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IDEAS '17 Paper Acceptance Rate 38 of 102 submissions, 37%;

Overall Acceptance Rate 74 of 210 submissions, 35%

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Cited By

Pulikottil TEstrada-Jimenez LAbadía JCarrera-Rivera ATorayev ARehman HMo FNikghadam-Hojjati SBarata J(2023)Big Data Life Cycle in Shop-Floor–Trends and ChallengesIEEE Access10.1109/ACCESS.2023.325328611(30008-30026)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3253286
Yang CXu XNunes BSiqueira S(2023)Bubbles bursting: Investigating and measuring the personalisation of social media searchesTelematics and Informatics10.1016/j.tele.2023.10199982(101999)Online publication date: Aug-2023
https://doi.org/10.1016/j.tele.2023.101999
Mohammed LAlHabshy AElDahshan K(2022)Big Data Visualization: A Survey2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)10.1109/HORA55278.2022.9799819(1-12)Online publication date: 9-Jun-2022
https://doi.org/10.1109/HORA55278.2022.9799819
Friske MWirfs-Brock JDevendorf LWakkary RAndersen KOdom WDesjardins APetersen M(2020)Entangling the Roles of Maker and Interpreter in Interpersonal Data NarrativesProceedings of the 2020 ACM Designing Interactive Systems Conference10.1145/3357236.3395442(297-310)Online publication date: 3-Jul-2020
https://dl.acm.org/doi/10.1145/3357236.3395442
DSouza JVelan S. S(2020)Using Exploratory Data Analysis for Generating Inferences on the Correlation of COVID-19 cases2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT49239.2020.9225621(1-6)Online publication date: Jul-2020
https://doi.org/10.1109/ICCCNT49239.2020.9225621
Reynoso MDivan M(2020)Applying Data Visualization Guideline on Forest Fires in Argentina2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)10.1109/Confluence47617.2020.9058174(617-622)Online publication date: Jan-2020
https://doi.org/10.1109/Confluence47617.2020.9058174
Kahil MBouramoul ADerdour M(2020)Big Data and Interactive Visualization: Overview on Challenges, Techniques and ToolsAdvanced Intelligent Systems for Sustainable Development (AI2SD’2019)10.1007/978-3-030-36674-2_17(157-167)Online publication date: 6-Feb-2020
https://doi.org/10.1007/978-3-030-36674-2_17
Kahil MBouramoul ADerdour M(2019)Towards a New Architecture for Data Multilevels Interactive Visualization in Big Data Domains2019 International Conference on Networking and Advanced Systems (ICNAS)10.1109/ICNAS.2019.8807847(1-7)Online publication date: Jun-2019
https://doi.org/10.1109/ICNAS.2019.8807847
Fahad SYahya A(2018)Big Data Visualization: Allotting by R and Python with GUI Tools2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE)10.1109/ICSCEE.2018.8538413(1-8)Online publication date: Jul-2018
https://doi.org/10.1109/ICSCEE.2018.8538413

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