Visualizing uncertainty in flow diagrams - a case study in product costing.

Zana Vosough, Dietrich Kammer, Mandy Keck, Rainer Groh

Research output: Contribution to conferencePaperpeer-review

9 Citations (Scopus)

Abstract

Business Intelligence applications often handle data sets that contain uncertain values. In this work 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 provide the uncertainty information for single items, decision makers need to quickly understand the cost uncertainties within the hierarchical data structure of the complete product. To provide this kind of quick overview, we propose a holistic visualization that contains both data and uncertainty. Since Flow diagrams are suitable to visualize tree data structures associated with value attributes, we focus on incorporating uncertainty information directly into these diagrams. Interviews with product costing experts led us to base our solution on Sankey diagrams. We chose three visualization techniques that are able to convey uncertainty information to the user: Color-code, Gradient, and Margin. We contribute a user study, which involved solving different product costing tasks using these three different visualizations. From the recorded error rates and subjective feedback, we designed an integrated approach that combines elements from all three distinct techniques.

Original languageEnglish
Pages1-8
Number of pages8
DOIs
Publication statusPublished - 14 Aug 2017
Externally publishedYes
Event10th International Symposium on Visual Information Communication and Interaction - Bangkok, Thailand
Duration: 14 Aug 2017 → …

Conference

Conference10th International Symposium on Visual Information Communication and Interaction
Abbreviated titleVinci 2017
Country/TerritoryThailand
CityBangkok
Period14.08.2017 → …

Keywords

  • Business Intelligence
  • Flow diagrams
  • Product Costing
  • Sankey diagrams
  • Uncertainty

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