research-article

Foresight: recommending visual insights

Authors:

Çağatay Demiralp,

Peter J. Haas,

Srinivasan Parthasarathy,

Tejaswini PedapatiAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 10, Issue 12

Pages 1937 - 1940

https://doi.org/10.14778/3137765.3137813

Published: 01 August 2017 Publication History

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Abstract

Current tools for exploratory data analysis (EDA) require users to manually select data attributes, statistical computations and visual encodings. This can be daunting for large-scale, complex data. We introduce Foresight, a system that helps the user rapidly discover visual insights from large high-dimensional datasets. Formally, an "insight" is a strong manifestation of a statistical property of the data, e.g., high correlation between two attributes, high skewness or concentration about the mean of a single attribute, a strong clustering of values, and so on. For each insight type, Foresight initially presents visualizations of the top k instances in the data, based on an appropriate ranking metric. The user can then look at "nearby" insights by issuing "insight queries" containing constraints on insight strengths and data attributes. Thus the user can directly explore the space of insights, rather than the space of data dimensions and visual encodings as in other visual recommender systems. Foresight also provides "global" views of insight space to help orient the user and ensure a thorough exploration process. Furthermore, Foresight facilitates interactive exploration of large datasets through fast, approximate sketching.

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Xing JWang XJagadish H(2024)Data-Driven Insight Synthesis for Multi-Dimensional DataProceedings of the VLDB Endowment10.14778/3641204.364121117:5(1007-1019)Online publication date: 1-Jan-2024
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Information & Contributors

Information

Published In

cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 10, Issue 12

August 2017

427 pages

ISSN:2150-8097

Editors:
Peter Boncz
CWI
,
Ken Salem
University of Waterloo

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VLDB Endowment

Publication History

Published: 01 August 2017

Published in PVLDB Volume 10, Issue 12

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Xing JWang XJagadish H(2024)Data-Driven Insight Synthesis for Multi-Dimensional DataProceedings of the VLDB Endowment10.14778/3641204.364121117:5(1007-1019)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.14778/3641204.3641211
Rezaie MTory MCarpendale S(2024)Struggles and Strategies in Understanding Information VisualizationsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.338856030:6(3035-3048)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1109/TVCG.2024.3388560
Epperson WGorantla VMoritz DPerer A(2024)Dead or Alive: Continuous Data Profiling for Interactive Data ScienceIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332736730:1(197-207)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TVCG.2023.3327367
Shi YChen BChen YJin ZXu KJiao XGao TCao N(2024)Supporting Guided Exploratory Visual Analysis on Time Series Data with Reinforcement LearningIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332720030:1(1172-1182)Online publication date: 1-Jan-2024
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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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