SellTrend: Inter-Attribute Visual Analysis of Temporal Transaction Data

Liu, Zhicheng; Stasko, John; Sullivan, Timothy M.

doi:10.1109/tvcg.2009.180

Cited by 19 publications

(2 citation statements)

References 29 publications

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“…Studying, analysing and visualizing such data are critical for understanding the underlying business processes. Recent work on both multi-variate and categorical data [ZSS09], [AGMS11], [TLLH12] acknowledges the importance for tools that support understanding these kinds of data sets.…”

Section: Introductionmentioning

confidence: 99%

Visual Analysis of Multi‐Dimensional Categorical Data Sets

Broeksema

Telea

Baudel

2013

Computer Graphics Forum

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We present a set of interactive techniques for the visual analysis of multidimensional categorical data. Our approach is based on Multiple Correspondence Analysis (MCA), which allows one to analyze relationships, patterns, trends and outliers among dependent categorical variables. We use MCA as a dimensionality reduction technique to project both observations and their attributes in the same 2D space. We use a treeview to show attributes and their domains, a histogram of their representativity in the dataset, and as a compact overview of attribute-related facts. A second view shows both attributes and observations. We use a Voronoi diagram whose cells can be interactively merged to discover salient attributes, cluster values, and bin categories. Barchart legends help assigning meaning to the 2D view axes and 2D point clusters. We illustrate our techniques with real-world application data.

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Section: Introductionmentioning

confidence: 99%

Visual Analysis of Multi‐Dimensional Categorical Data Sets

Broeksema

Telea

Baudel

2013

Computer Graphics Forum

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“…Additionally, cuing needs to be sensitive to complicated instances, for example the biomedical specialist’s flow of animation-based comparisons. Liu and Stasko underscore the difficulty a scientist faces in keeping track of such animated comparisons (40). As they note, “It is non-trivial to identify the combination of values that actually lead to [an] abnormality;” and to keep track of these values visual cues are essential (40, 1027).…”

Section: Discussionmentioning

confidence: 99%

A cognitive task analysis of a visual analytic workflow: Exploring molecular interaction networks in systems biology

et al. 2011

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Background: Bioinformatics visualization tools are often not robust enough to support biomedical specialists’ complex exploratory analyses. Tools need to accommodate the workflows that scientists actually perform for specific translational research questions. To understand and model one of these workflows, we conducted a case-based, cognitive task analysis of a biomedical specialist’s exploratory workflow for the question: What functional interactions among gene products of high throughput expression data suggest previously unknown mechanisms of a disease? Results: From our cognitive task analysis four complementary representations of the targeted workflow were developed. They include: usage scenarios, flow diagrams, a cognitive task taxonomy, and a mapping between cognitive tasks and user-centered visualization requirements. The representations capture the flows of cognitive tasks that led a biomedical specialist to inferences critical to hypothesizing. We created representations at levels of detail that could strategically guide visualization development, and we confirmed this by making a trial prototype based on user requirements for a small portion of the workflow. Conclusions: Our results imply that visualizations should make available to scientific users “bundles of features” consonant with the compositional cognitive tasks purposefully enacted at specific points in the workflow. We also highlight certain aspects of visualizations that: (a) need more built-in flexibility; (b) are critical for negotiating meaning; and (c) are necessary for essential metacognitive support.

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