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Avoiding drill-down fallacies with VisPilot: assisted exploration of data subsets

Authors:

Doris Jung-Lin Lee,

Hazem Elmeleegy,

Aditya ParameswaranAuthors Info & Claims

IUI '19: Proceedings of the 24th International Conference on Intelligent User Interfaces

Pages 186 - 196

https://doi.org/10.1145/3301275.3302307

Published: 17 March 2019 Publication History

Abstract

As datasets continue to grow in size and complexity, exploring multi-dimensional datasets remain challenging for analysts. A common operation during this exploration is drill-down-understanding the behavior of data subsets by progressively adding filters. While widely used, in the absence of careful attention towards confounding factors, drill-downs could lead to inductive fallacies. Specifically, an analyst may end up being "deceived" into thinking that a deviation in trend is attributable to a local change, when in fact it is a more general phenomenon; we term this the drill-down fallacy. One way to avoid falling prey to drill-down fallacies is to exhaustively explore all potential drill-down paths, which quickly becomes infeasible on complex datasets with many attributes. We present VisPilot, an accelerated visual data exploration tool that guides analysts through the key insights in a dataset, while avoiding drill-down fallacies. Our user study results show that VisPilot helps analysts discover interesting visualizations, understand attribute importance, and predict unseen visualizations better than other multidimensional data analysis baselines.

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Cited By

Li HChalapathi NQu HCheung AParameswaran ASerra ESpezzano F(2024)Inferring Visualization Intent from ConversationProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679589(1184-1194)Online publication date: 21-Oct-2024
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Lin YLi HYang LWu AQu H(2024)InkSight: Leveraging Sketch Interaction for Documenting Chart Findings in Computational NotebooksIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332717030:1(944-954)Online publication date: 1-Jan-2024
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Index Terms

Avoiding drill-down fallacies with VisPilot: assisted exploration of data subsets
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods
      1. User models
      2. User studies
  2. Visualization
    1. Visualization application domains
      1. Information visualization
      2. Visual analytics

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cover image ACM Conferences

IUI '19: Proceedings of the 24th International Conference on Intelligent User Interfaces

March 2019

713 pages

ISBN:9781450362726

DOI:10.1145/3301275

General Chairs:
Wai-Tat Fu,
Shimei Pan,
Program Chairs:
Oliver Brdiczka,
Polo Chau,
Gaelle Calvary

Copyright © 2019 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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Published: 17 March 2019

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Microsoft
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IUI '19: 24th International Conference on Intelligent User Interfaces

March 17 - 20, 2019

California, Marina del Ray

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IUI '19 Paper Acceptance Rate 71 of 282 submissions, 25%;

Overall Acceptance Rate 746 of 2,811 submissions, 27%

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30th International Conference on Intelligent User Interfaces

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Cited By

Li HChalapathi NQu HCheung AParameswaran ASerra ESpezzano F(2024)Inferring Visualization Intent from ConversationProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679589(1184-1194)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679589
Wu GGuo SHoffswell JChan GRossi RKoh E(2024)Socrates: Data Story Generation via Adaptive Machine-Guided Elicitation of User FeedbackIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332736330:1(131-141)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TVCG.2023.3327363
Lin YLi HYang LWu AQu H(2024)InkSight: Leveraging Sketch Interaction for Documenting Chart Findings in Computational NotebooksIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332717030:1(944-954)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TVCG.2023.3327170
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Li XHung YPan JLin W(2024)VisCollage: Annotative Collages for Organizing Data Event Charts2024 IEEE 17th Pacific Visualization Conference (PacificVis)10.1109/PacificVis60374.2024.00036(262-271)Online publication date: 23-Apr-2024
https://doi.org/10.1109/PacificVis60374.2024.00036
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Silva-Blancas VÁlvarez-Alvarado JHerrera-Navarro ARodríguez-Reséndiz J(2023)Tendency on the Application of Drill-Down Analysis in Scientific Studies: A Systematic ReviewTechnologies10.3390/technologies1104011211:4(112)Online publication date: 13-Aug-2023
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Li WSchöttler SScott-Brown JWang YChen SQu HBach B(2023)NetworkNarratives: Data Tours for Visual Network Exploration and AnalysisProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581452(1-15)Online publication date: 19-Apr-2023
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Cabric FBjarnadóttir MCabouat AIsenberg P(2023)Open Questions About the Visualization of Sociodemographic Data2023 IEEE Workshop on Visualization for Social Good (VIS4Good)10.1109/VIS4Good60218.2023.00010(16-20)Online publication date: 22-Oct-2023
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