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Visualizing uncertainty in flow diagrams: a case study in product costing

Published:14 August 2017Publication History

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.

References

  1. Jeroen CJH Aerts, Keith C Clarke, and Alex D Keuper. 2003. Testing popular visualization techniques for representing model uncertainty. Cartography and Geographic Information Science 30, 3 (2003), 249--261.Google ScholarGoogle ScholarCross RefCross Ref
  2. Ann M Bisantz, T Kesevadas, Peter Scott, David Lee, Santosh Basapur, Parijat Bhide, P Bhide, and P Bhide. 2002. Holistic battlespace visualization: advanced concepts in information visualization and cognitive studies. U. Buffalo (2002).Google ScholarGoogle Scholar
  3. Steve Blenkinsop, Pete Fisher, Lucy Bastin, and Jo Wood. 2000. Evaluating the perception of uncertainty in alternative visualization strategies. Cartographica: The International Journal for Geographic Information and Geovisualization 37, 1 (2000), 1--14.Google ScholarGoogle ScholarCross RefCross Ref
  4. Nadia Boukhelifa, Anastasia Bezerianos, Tobias Isenberg, and Jean-Daniel Fekete. 2012. Evaluating sketchiness as a visual variable for the depiction of qualitative uncertainty. IEEE Transactions on Visualization and Computer Graphics 18, 12 (2012), 2769--2778. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Ross Brown. 2004. Animated visual vibrations as an uncertainty visualisation technique. In Proceedings of the 2nd international conference on Computer graphics and interactive techniques in Australasia and South East Asia. ACM, 84--89. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Barbara P Buttenfield and John H Ganter. 1990. Visualization and GIS: What should we see? What might we miss. In Proceedings of the 4th International Symposium on Spatial Data Handling, Vol. 1. 307--316.Google ScholarGoogle Scholar
  7. Andrej Cedilnik and Penny Rheingans. 2000. Procedural annotation of uncertain information. In Visualization 2000. Proceedings. IEEE, 77--84. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Nathaniel Cesario, Alex Pang, and Lisa Singh. 2011. Visualizing node attribute uncertainty in graphs. In IS&T/SPIE Electronic Imaging. International Society for Optics and Photonics, 78680H--78680H.Google ScholarGoogle Scholar
  9. Christopher Collins, M Sheelagh T Carpendale, and Gerald Penn. 2007. Visualization of uncertainty in lattices to support decision-making.. In EuroVis. 51--58. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Michael Correll and Michael Gleicher. 2014. Error bars considered harmful: Exploring alternate encodings for mean and error. IEEE transactions on visualization and computer graphics 20, 12 (2014), 2142--2151.Google ScholarGoogle Scholar
  11. Charles R Ehlschlaeger, Ashton M Shortridge, and Michael F Goodchild. 1997. Visualizing spatial data uncertainty using animation. Computers & Geosciences 23, 4 (1997), 387--395. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Holger Eichelberger. 2003. Nice class diagrams admit good design?. In Proceedings of the 2003 ACM symposium on Software visualization. ACM, 159-ff. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Nahum Gershon. 1998. Visualization of an imperfect world. IEEE Computer Graphics and Applications 18, 4 (1998), 43--45. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Henning Griethe, Heidrun Schumann, and others. 2006. The visualization of uncertain data: Methods and problems.. In SimVis. 143--156.Google ScholarGoogle Scholar
  15. Theresia Gschwandtnei, Markus Bögl, Paolo Federico, and Silvia Miksch. 2016. Visual encodings of temporal uncertainty: A comparative user study. IEEE transactions on visualization and computer graphics 22, 1 (2016), 539--548.Google ScholarGoogle Scholar
  16. Stefan Hesse. 2015. Struktur und Gestaltung von Informationsvisualisierungen zur Entscheidungsunterstützung. phdthesis. Technischen Universität Dresden. http://slubdd.de/katalog?TN_libero_mab216239713Google ScholarGoogle Scholar
  17. Danny Holten. 2006. Hierarchical edge bundles: Visualization of adjacency relations in hierarchical data. IEEE Transactions on visualization and computer graphics 12, 5 (2006), 741--748. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Gary J Hunter. 1999. New tools for handling spatial data quality: moving from academic concepts to practical reality. URISA Journal 11, 2 (1999), 25--34.Google ScholarGoogle Scholar
  19. Gary J Hunter and MF Goodchild. 1993. Managing uncertainty in spatial databases: Putting theory into practice. In Papers from the Annual Conference-Urban and Regional Information Systems Association. URISA URBAN AND REGIONAL INFORMATION SYSTEMS, 15-15.Google ScholarGoogle Scholar
  20. Chris Johnson, Robert Moorhead, Tamara Munzner, Hanspeter Pfister, Penny Rheingans, and Terry S Yoo. 2006. NIH/NSF visualization research challenges report. In Los Alamitos, Ca: IEEE Computing Society. Citeseer.Google ScholarGoogle Scholar
  21. Chris R Johnson and Allen R Sanderson. 2003. A next step: Visualizing errors and uncertainty. IEEE Computer Graphics and Applications 23, 5 (2003), 6--10. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Bettina Laugwitz, Theo Held, and Martin Schrepp. 2008. Construction and Evaluation of a User Experience Questionnaire. Springer Berlin Heidelberg, Berlin, Heidelberg, 63--76. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Bongshin Lee, George G Robertson, Mary Czerwinski, and Cynthia Sims Parr. 2007. CandidTree: visualizing structural uncertainty in similar hierarchies. Information Visualization 6, 3 (2007), 233--246. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Suresh K Lodha, Alex Pang, Robert E Sheehan, and Craig M Wittenbrink. 1996. UFLOW: Visualizing uncertainty in fluid flow. In Proceedings of the 7th conference on Visualization'96. IEEE Computer Society Press, 249-ff. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Alan M MacEachren. 1992. Visualizing uncertain information. Cartographic Perspectives 13 (1992), 10--19.Google ScholarGoogle ScholarCross RefCross Ref
  26. Alan M MacEachren, Cynthia A Brewer, and Linda W Pickle. 1998. Visualizing georeferenced data: representing reliability of health statistics. Environment and planning A 30, 9 (1998), 1547--1561.Google ScholarGoogle Scholar
  27. Alan M MacEachren, Anthony Robinson, Susan Hopper, Steven Gardner, Robert Murray, Mark Gahegan, and Elisabeth Hetzler. 2005. Visualizing geospatial information uncertainty: What we know and what we need to know. Cartography and Geographic Information Science 32, 3 (2005), 139--160.Google ScholarGoogle ScholarCross RefCross Ref
  28. Andrew W Marshall and William H Meckling. 1962. Predictability of the costs, time, and success of development. In The rate and direction of inventive activity: Economic and social factors. Princeton University Press, 461--476.Google ScholarGoogle Scholar
  29. Edward J Mulrow. 2002. The visual display of quantitative information. (2002).Google ScholarGoogle Scholar
  30. Limor Nadav-Greenberg and Susan L Joslyn. 2009. Uncertainty forecasts improve decision making among nonexperts. Journal of Cognitive Engineering and Decision Making 3, 3 (2009), 209--227.Google ScholarGoogle ScholarCross RefCross Ref
  31. Chris Olston and Jock D Mackinlay. 2002. Visualizing data with bounded uncertainty. In Information Visualization, 2002. INFOVIS 2002. IEEE Symposium on. IEEE, 37--40. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Alex T Pang, Craig M Wittenbrink, and Suresh K Lodha. 1997. Approaches to uncertainty visualization. The Visual Computer 13, 8 (1997), 370--390.Google ScholarGoogle ScholarCross RefCross Ref
  33. Patrick Riehmann, Manfred Hanfler, and Bernd Froehlich. 2005. Interactive Sankey Diagrams. In Proceedings of the IEEE Symposium on Information Visualization, John Stasko and Matthew O. Ward (Eds.). IEEE, 233--240. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Ben Shneiderman. 1996. The eyes have it: A task by data type taxonomy for information visualizations. In Visual Languages, 1996. Proceedings., IEEE Symposium on. IEEE, 336--343. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Meredith Skeels, Bongshin Lee, Greg Smith, and George G Robertson. 2010. Revealing uncertainty for information visualization. Information Visualization 9, 1 (2010), 70--81. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Alexander Streit, Binh Pham, and Ross Brown. 2008. A spreadsheet approach to facilitate visualization of uncertainty in information. IEEE transactions on visualization and computer graphics 14, 1 (2008), 61--72. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Susanne Tak, Alexander Toet, and Jan van Erp. 2014. The perception of visual uncertainty representation by non-experts. IEEE transactions on visualization and computer graphics 20, 6 (2014), 935--943. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Barry N Taylor and Chris E Kuyatt. 1994. Guidelines for evaluating and expressing the uncertainty of NIST measurement results. US Department of Commerce, Technology Administration, National Institute of Standards and Technology Gaithersburg, MD.Google ScholarGoogle Scholar
  39. Judi Thomson, Elizabeth Hetzler, Alan MacEachren, Mark Gahegan, and Misha Pavel. 2005. A typology for visualizing uncertainty. In Electronic Imaging 2005. International Society for Optics and Photonics, 146--157.Google ScholarGoogle Scholar
  40. Tatiana Von Landesberger, Arjan Kuijper, Tobias Schreck, Jörn Kohlhammer, Jarke J van Wijk, J-D Fekete, and Dieter W Fellner. 2011. Visual analysis of large graphs: state-of-the-art and future research challenges. In Computer graphics forum, Vol. 30. Wiley Online Library, 1719--1749.Google ScholarGoogle Scholar
  41. Zana Vosough, Rainer Groh, and Hans-Jörg Schulz. 2017. On Establishing Visualization Requirements: A Case Study in Product Costing. In Eurographics Conference on Visualization (EuroVis): Short Papers. The Eurographics Association, to appear.Google ScholarGoogle Scholar
  42. Craig Michael Wittenbrink, Alex Tiu Pang, and Suresh K Lodha. 1995. Verity visualization: Visual mappings. Computer Research Laboratory {University of California, Santa Cruz}.Google ScholarGoogle Scholar
  43. Craig M Wittenbrink, Alex T Pang, and Suresh K Lodha. 1996. Glyphs for visualizing uncertainty in vector fields. IEEE transactions on Visualization and Computer Graphics 2, 3 (1996), 266--279. Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. Zaixian Xie, Shiping Huang, Matthew O Ward, and Elke A Rundensteiner. 2006. Exploratory visualization of multivariate data with variable quality. In Visual Analytics Science And Technology, 2006 IEEE Symposium On. IEEE, 183--190.Google ScholarGoogle ScholarCross RefCross Ref

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            cover image ACM Other conferences
            VINCI '17: Proceedings of the 10th International Symposium on Visual Information Communication and Interaction
            August 2017
            158 pages
            ISBN:9781450352925
            DOI:10.1145/3105971

            Copyright © 2017 ACM

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            Publication History

            • Published: 14 August 2017

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            VINCI '17 Paper Acceptance Rate12of27submissions,44%Overall Acceptance Rate71of193submissions,37%

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