Tessera: Discretizing Data Analysis Workflows on a Task Level

要旨

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.

著者
Jing Nathan Yan
Cornell University, Itahca, New York, United States
Ziwei Gu
Cornell University, Ithaca, New York, United States
Jeffrey M. Rzeszotarski
Cornell University, Ithaca, New York, United States
DOI

10.1145/3411764.3445728

論文URL

https://doi.org/10.1145/3411764.3445728

動画

会議: CHI 2021

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2021.acm.org/)

セッション: Designing Effective Visualizations

[A] Paper Room 09, 2021-05-13 17:00:00~2021-05-13 19:00:00 / [B] Paper Room 09, 2021-05-14 01:00:00~2021-05-14 03:00:00 / [C] Paper Room 09, 2021-05-14 09:00:00~2021-05-14 11:00:00
Paper Room 09
13 件の発表
2021-05-13 17:00:00
2021-05-13 19:00:00
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