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Visualizing two cost calculation versions with Sankey diagrams. Version 1 is shown on the left side (orange), and version 2 on the right side (green). It shows a comparison between the items on, a) the first level, b) the second level, and c) the third level, close to each other along with their sub-items.
Contexts in source publication
Context 1
... rationale behind this design is that users usually want to identify the differences between the total costs at the first glance. By putting the par- ent nodes (bars) close to each other, this comparison becomes easier (see Figure 2). ...
Context 2
... this strategy follows the juxtaposi- tion strategy, due to the interaction with the different views, it still conveys the feeling that the images are overlaid (Roberts, 2004). Figure 2 shows an example of two versions of a cost calculation for an industrial pump from different points in time. The first version represents the items on the left side in orange and the second version on the right side in green. ...
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Citations
... Sankey diagrams and their variants [1,4,8,9,[11][12][13][14][15] have been an area of significant research in data visualization and utilized to study different applications [2,3,[5][6][7]10]. An important question researchers have studied along this topic is how to convey more information concerning data flow. ...
We present the hierarchical Sankey diagram that aims to augment the original Sankey diagram by enabling users to examine inflow links and levels of detail through four different variants. We provide the details of our design along with results on a student course performance dataset. Finally, the effectiveness of the four variants for the hierarchical Sankey diagram is evaluated via a user study.
... All solutions are designed to give a quick impression of the [44][45][46][47][48][49][50][51][52][53][54] uncertainty in the data and then find important items to investigate further. Although a tooltip was integrated in all solutions, our study does not depend on the tooltip information and was not made available to the participants. ...
... Building upon our previous work that introduced a mirroring technique for Sankey diagrams to show two dimensions of the data [48], in this work we use Parallel Sets to represent more aspects of costing data. Fig. 7 shows one example of applying our technique to the ribbons of Parallel Sets visualizing selected data from the same industrial pump and its different dimensions such as country of origin, price range, and price type. ...
Business Intelligence applications often handle data sets that contain uncertain values. In this contribution, we focus on product costing, which deals with the average costs of product components-that vary significantly based on many factors such as inflation, exchange rates, and commodity prices. After experts estimate the uncertainty information for single items, decision makers need to quickly ascertain the cost uncertainties within the hierarchical data structure of the complete product. We propose that only a holistic visualization containing both data and uncertainty can provide this kind of quick overview. Such a visualization must be able to visualize tree data structures associated with value attributes. After conducting interviews with product costing experts, we focused on Flow diagrams, which fulfill this basic requirement. However, they need to be extended in order to directly incorporate uncertainty information. We investigated three visualization techniques applicable to the ribbons of Flow diagrams to convey uncertainty information: Color-code, Gradient, and Margin. Moreover, we designed five visual approaches to show the uncertainty on nodes of Flow diagrams that we evaluated with visualization experts. The approaches add different geometries to the nodes such as triangles, blocks, or forks. The preferred solutions for the nodes was adding forks or filled blocks. With regards to the ribbons, we contribute a user study involving the solution of different product costing tasks using the three different visualizations. Although Gradient was considered an intuitive choice to show uncertainty, it yielded the highest error rates. In contrast, Color-code and Margin were superior depending on the performed task. Based on these findings and the subjective feedback, we designed an integrated approach that combines elements from all three distinct techniques and applied it to Sankey diagrams and Parallel sets.
