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Tessera: Discretizing Data Analysis Workflows on a Task Level

Authors:

Jing Nathan Yan,

Jeffrey M RzeszotarskiAuthors Info & Claims

CHI '21: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

Article No.: 20, Pages 1 - 15

https://doi.org/10.1145/3411764.3445728

Published: 07 May 2021 Publication History

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Abstract

Researchers have investigated a number of strategies for capturing and analyzing data analyst event logs in order to design better tools, identify failure points, and guide users. However, this remains challenging because individual- and session-level behavioral differences lead to an explosion of complexity and there are few guarantees that log observations map to user cognition. In this paper we introduce a technique for segmenting sequential analyst event logs which combines data, interaction, and user features in order to create discrete blocks of goal-directed activity. Using measures of inter-dependency and comparisons between analysis states, these blocks identify patterns in interaction logs coupled with the current view that users are examining. Through an analysis of publicly available data and data from a lab study across a variety of analysis tasks, we validate that our segmentation approach aligns with users’ changing goals and tasks. Finally, we identify several downstream applications for our approach.

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Cited By

Harrison GBryson KBamba ADovichi LBinion ABorem AUr B(2024)JupyterLab in Retrograde: Contextual Notifications That Highlight Fairness and Bias Issues for Data ScientistsProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642755(1-19)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642755
Lee SLaw MHoffman G(2024)When and How to Use AI in the Design Process? Implications for Human-AI Design CollaborationInternational Journal of Human–Computer Interaction10.1080/10447318.2024.2353451(1-16)Online publication date: 22-May-2024
https://doi.org/10.1080/10447318.2024.2353451
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https://doi.org/10.3233/SW-233407
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Index Terms

Tessera: Discretizing Data Analysis Workflows on a Task Level
1. Human-centered computing
  1. Human computer interaction (HCI)
2. Information systems
  1. Information systems applications

Index terms have been assigned to the content through auto-classification.

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cover image ACM Conferences

CHI '21: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

May 2021

10862 pages

ISBN:9781450380966

DOI:10.1145/3411764

General Chairs:
Yoshifumi Kitamura
Tohoku University, Japan
,
Aaron Quigley
University of New South Wales, Australia
,
Program Chairs:
Katherine Isbister
University of California Santa Cruz, USA
,
Takeo Igarashi
The University of Tokyo, Japan
,
Publications Chairs:
Pernille Bjørn
University of Copenhagen, Denmark
,
Steven Drucker
Microsoft Research, USA

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Cited By

Harrison GBryson KBamba ADovichi LBinion ABorem AUr B(2024)JupyterLab in Retrograde: Contextual Notifications That Highlight Fairness and Bias Issues for Data ScientistsProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642755(1-19)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642755
Lee SLaw MHoffman G(2024)When and How to Use AI in the Design Process? Implications for Human-AI Design CollaborationInternational Journal of Human–Computer Interaction10.1080/10447318.2024.2353451(1-16)Online publication date: 22-May-2024
https://doi.org/10.1080/10447318.2024.2353451
Daga EGroth P(2023)Data journeys: Explaining AI workflows through abstractionSemantic Web10.3233/SW-233407(1-27)Online publication date: 15-Jun-2023
https://doi.org/10.3233/SW-233407
Gathani SMonadjemi SOttley ABattle L(2022)A Grammar‐Based Approach for Applying Visualization Taxonomies to Interaction LogsComputer Graphics Forum10.1111/cgf.1455741:3(489-500)Online publication date: 29-Jul-2022
https://doi.org/10.1111/cgf.14557

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