Taggle: Combining overview and details in tabular data visualizations

Furmanová, Katarína; Gratzl, Samuel; Stitz, Holger; Zichner, Thomas; Jarešová, Miroslava; Lex, Alexander; Streit, Marc

doi:10.1177/1473871619878085

Cited by 35 publications

(29 citation statements)

References 59 publications

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“…There are numerous extensions and generalizations of our work, ranging from implementing more brushing tools, such as lasso selections, to allowing analysts to filter datasets. We argue that our framework could be extended to other visualization techniques, such as parallel coordinates, histograms, or tabular visualizations 67 with small adaptions. Other visualization techniques could also provide additional clues we could use for predicting intents.…”

Section: Discussionmentioning

confidence: 99%

Predicting intent behind selections in scatterplot visualizations

Gadhave

Görtler

Cutler

et al. 2021

Information Visualization

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Predicting and capturing an analyst’s intent behind a selection in a data visualization is valuable in two scenarios: First, a successful prediction of a pattern an analyst intended to select can be used to auto-complete a partial selection which, in turn, can improve the correctness of the selection. Second, knowing the intent behind a selection can be used to improve recall and reproducibility. In this paper, we introduce methods to infer analyst’s intents behind selections in data visualizations, such as scatterplots. We describe intents based on patterns in the data, and identify algorithms that can capture these patterns. Upon an interactive selection, we compare the selected items with the results of a large set of computed patterns, and use various ranking approaches to identify the best pattern for an analyst’s selection. We store annotations and the metadata to reconstruct a selection, such as the type of algorithm and its parameterization, in a provenance graph. We present a prototype system that implements these methods for tabular data and scatterplots. Analysts can select a prediction to auto-complete partial selections and to seamlessly log their intents. We discuss implications of our approach for reproducibility and reuse of analysis workflows. We evaluate our approach in a crowd-sourced study, where we show that auto-completing selection improves accuracy, and that we can accurately capture pattern-based intent.

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Section: Discussionmentioning

confidence: 99%

Predicting intent behind selections in scatterplot visualizations

Gadhave

Görtler

Cutler

et al. 2021

Information Visualization

Self Cite

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“…To identify outliers or to assess single data points, users need to move from the aggregated level down to the level of individual items. We use Taggle (Furmanova et al, 2020), a tabular visualization technique in which the items of the cohorts are displayed. Users can select attributes, whose data is displayed in the table, and sort, filter, and group the data (see Supplementary Fig.…”

Section: Software Descriptionmentioning

confidence: 99%

Coral: a web-based visual analysis tool for creating and characterizing cohorts

Adelberger

Eckelt

Bauer

et al. 2021

Preprint

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Summary: A main task in computational cancer analysis is the identification of patient subgroups (i.e., cohorts) based on metadata attributes (patient stratification) or genomic markers of response (biomarkers). Coral is a web-based cohort analysis tool that is designed to support this task: Users can interactively create and refine cohorts, which can then be compared, characterized, and inspected down to the level of single items. We visualize the evolution of cohorts and also provide intuitive access to prevalence information. Coral can be utilized to explore any type of cohort and sample set. Here, we focus on the analysis of genomic data from cancer patients and therefore included in the public version data from the AACR Project GENIE, The Cancer Genome Atlas, and the Cell Line Encyclopedia. Availability and Implementation: Coral is publicly available at https://coral.caleydoapp.org. The source code is released at https://github.com/Caleydo/coral.

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“…A simple but efficient way of encoding node attributes in adjacency matrices is using juxtaposed tables. We juxtapose a tabular visualization [29,12] with the matrix, and keep the rows consistent between the matrix and the table [20,31,5], as shown in Figure 2. As a result, we can use highly efficient encodings for the attributes.…”

Section: Adjacency Matrixmentioning

confidence: 99%