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Supporting Decision Making in Engineering Design Using Parallel Coordinates in Virtual Reality

Published Online:https://doi.org/10.2514/1.J060441

Computational engineering design methods and tools are common practices in the modern industry. Such approaches are integral in enabling designers to efficiently explore large and complex design spaces. However, they also tend to dramatically increase the number of candidate solutions that decision makers must correctly interpret. Because all candidate solutions can be represented in a digital form together with their assessment criteria, a natural way to explore and understand the complexities of the design problem is to visualize their multidimensional nature. The task now involves the discovery of patterns and trends within a multidimensional design space. This work aims to enhance the design decision-making process with immersive Parallel Coordinates Plot (IPCP) in virtual reality. This paper presents the design of this system, which allows representation and exploration of multidimensional scientific datasets. A qualitative validation with two surrogate expert users demonstrated that the system can be used successfully to detect both known and previously unknown patterns and support learning the decision-making process in a shorter time. The results serve as a promising indication of how immersive parallel coordinate plots can enhance decision support in complex engineering design processes.

References

  • [1] Kipouros T., Inselberg A., Parks G. T. and Savill M., “Parallel Coordinates in Computational Engineering Design,” AIAA Multidisciplinary Design Optimization Specialists, AIAA Paper 2013-1750, 2013. https://doi.org/10.2514/6.2013-1750 Google Scholar

  • [2] Kipouros T., Jaeggi D., Dawes W., Parks G., Savill M. and Clarkson P., “Bi-Objective Design Optimization for Axial Compressors Using Tabu Search,” AIAA Journal, Vol. 46, No. 3, 2008, pp. 701–711. LinkGoogle Scholar

  • [3] Kipouros T., Jaeggi D., Dawes W., Parks G., Savill M. and Clarkson P., “Insight Into High-Quality Aerodynamic Design Spaces Through Multi-Objective Optimization,” CMES: Computer Modeling in Engineering and Sciences, Vol. 37, No. 1, 2008, pp. 1–44. Google Scholar

  • [4] D’Ambros A., Kipouros T., Zachos P., Savill M. and Benini E., “Computational Design Optimization for S-Ducts,” Designs, Vol. 2, No. 36, 2018, pp. 1–21. https://doi.org/10.3390/designs2040036 Google Scholar

  • [5] Tadeja S. K., Kipouros T. and Kristensson P. O., “Exploring Parallel Coordinates in Virtual Reality,” Extended Abstracts of the CHI Conference on Human Factors in Computing Systems Extended Abstracts (CHI EA’19), Assoc. for Computing Machinery Paper LBW2617, New York, 2019, pp. 1–6. https://doi.org/10.1145/3290607.3313068 Google Scholar

  • [6] Tadeja S. K., Kipouros T. and Kristensson P. O., “IPCP: Immersive Parallel Coordinates Plots for Engineering Design Processes,” Proceedings of AIAA SciTech Forum and Exposition, AIAA Paper 2020-0324, Jan. 2020. https://doi.org/10.2514/6.2020-0324 Google Scholar

  • [7] Tadeja S. K., Seshadri P. and Kristensson P. O., “Exploring Aerospace Design in Virtual Reality with Dimension Reduction,” Proceedings of AIAA SciTech Forum and Exposition, AIAA Paper 2019-2206, Jan. 2019. https://doi.org/10.2514/6.2019-2206 Google Scholar

  • [8] Tadeja S. K., Seshadri P. and Kristensson P. O., “AeroVR: An Immersive Visualisation System for Aerospace Design and Digital Twinning in Virtual Reality,” Aeronautical Journal, Vol. 124, No. 1280, 2020, pp. 1615–1635. CrossrefGoogle Scholar

  • [9] Inselberg A., “The Plane with Parallel Coordinates,” Visual Computer, Vol. 1, No. 2, 1985, pp. 69–91. https://doi.org/10.1007/BF01898350 CrossrefGoogle Scholar

  • [10] Inselberg A., Parallel Coordinates: Visual Multidimensional Geometry and Its Applications, 1st ed., Springer-Verlag, New York, 2009. CrossrefGoogle Scholar

  • [11] Wegenkittl R., Loffelmann H. and Groller E., “Visualizing the Behaviour of Higher Dimensional Dynamical Systems,” Proceedings of Visualization ’97 (Cat. No. 97CB36155), IEEE Computer Soc. Press, Washington, D.C., 1997, pp. 119–125. https://doi.org/10.1109/VISUAL.1997.663867 Google Scholar

  • [12] Gröller E., Löffelmann H. and Wegenkittl R., “Visualization of Analytically Defined Dynamical Systems,” Scientific Visualization Conference (dagstuhl ’97), IEEE Computer Soc. Press, Washington, D.C., 1997, pp. 71–71. Google Scholar

  • [13] Streit M., Ecker R. C., Österreicher K., Steiner G. E., Bischof H., Bangert C., Kopp T. and Rogojanu R., “3D Parallel Coordinate Systems, A New Data Visualization Method in the Context of Microscopy-Based Multicolor Tissue Cytometry,” Cytometry Part A, Vol. 69A, No. 7, 2006, pp. 601–611. https://doi.org/10.1002/cyto.a.20288 CrossrefGoogle Scholar

  • [14] Falkman G., “Information Visualisation in Clinical Odontology: Multidimensional Analysis and Interactive Data Exploration,” Artificial Intelligence in Medicine, Vol. 22, No. 2, 2001, pp. 133–158. https://doi.org/10.1016/S0933-3657(00)00104-4 CrossrefGoogle Scholar

  • [15] Ribarsky W., Ayers E., Eble J. and Mukherjea S., “Glyphmaker: Creating Customized Visualizations of Complex Data,” Computer, Vol. 27, No. 7, 1994, pp. 57–64. CrossrefGoogle Scholar

  • [16] Fanea E., Carpendale S. and Isenberg T., “An Interactive 3D Integration of Parallel Coordinates and Star Glyphs,” IEEE Symposium on Information Visualization, 2005 (INFOVIS 2005), IEEE Computer Soc. Press, Washington, D.C., 2005, pp. 149–156. https://doi.org/10.1109/INFVIS.2005.1532141 Google Scholar

  • [17] Johansson J., Ljung P., Jern M. and Cooper M., “Revealing Structure in Visualizations of Dense 2D and 3D Parallel Coordinates,” Information Visualization, Vol. 5, No. 2, 2006, pp. 125–136. https://doi.org/10.1057/palgrave.ivs.9500117 CrossrefGoogle Scholar

  • [18] Dang T. N., Wilkinson L. and Anand A., “Stacking Graphic Elements to Avoid Over-Plotting,” IEEE Transactions on Visualization and Computer Graphics, Vol. 16, No. 6, 2010, pp. 1044–1052. https://doi.org/10.1109/TVCG.2010.197 CrossrefGoogle Scholar

  • [19] Chang C., Dwyer T. and Marriott K., “An Evaluation of Perceptually Complementary Views for Multivariate Data,” 2018 IEEE Pacific Visualization Symposium (PacificVis), IEEE Computer Soc. Press, Washington, D.C., 2018, pp. 195–204. https://doi.org/10.1109/PacificVis.2018.00033 Google Scholar

  • [20] Johansson J., Forsell C. and Cooper M., “On the Usability of Three-Dimensional Display in Parallel Coordinates: Evaluating the Efficiency of Identifying Two-Dimensional Relationships,” Information Visualization, Vol. 13, No. 1, 2014, pp. 29–41. https://doi.org/10.1177/1473871613477091 CrossrefGoogle Scholar

  • [21] Holten D. and Wijk J. J. V., “Evaluation of Cluster Identification Performance for Different PCP Variants,” Computer Graphics Forum, Vol. 29, No. 3, 2010, pp. 793–802. https://doi.org/10.1111/j.1467-8659.2009.01666.x CrossrefGoogle Scholar

  • [22] Cordeil M., Cunningham A., Dwyer T., Thomas B. H. and Marriott K., “ImAxes: Immersive Axes as Embodied Affordances for Interactive Multivariate Data Visualisation,” Proceedings of the 30th Annual ACM Symposium on User Interface Software and Technology, Assoc. for Computing Machinery, New York, 2017, pp. 71–83. https://doi.org/10.1145/3126594.3126613 Google Scholar

  • [23] Butscher S., Hubenschmid S., Müller J., Fuchs J. and Reiterer H., “Clusters, Trends, and Outliers: How Immersive Technologies Can Facilitate the Collaborative Analysis of Multidimensional Data,” Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, Assoc. for Computing Machinery, New York, 2018, pp. 90:1–90:12. https://doi.org/10.1145/3173574.3173664 Google Scholar

  • [24] Rosenbaum R., Bottleson J., Liu Z. and Hamann B., “Involve Me and I Will Understand!: Abstract Data Visualization in Immersive Environments,” Proceedings of the 7th International Conference on Advances in Visual Computing–Volume Part I, Springer-Verlag, Berlin, 2011, pp. 530–540. Google Scholar

  • [25] Ribarsky W., Bolter J., Op Den Bosch A. and Van Teylingen R., “Visualization and Analysis Using Virtual Reality,” IEEE Computer Graphics and Applications, Vol. 14, No. 1, Jan. 1994, pp. 10–12. https://doi.org/10.1109/38.250911 Google Scholar

  • [26] Ishihara S., Ishihara’s Tests for Colour Deficiency, 38th ed., Kanehara Trading, Tokyo, 2017. Google Scholar

  • [27] Kennedy R. S., Lane N. E., Berbaum K. S. and Lilienthal M. G., “Simulator Sickness Questionnaire: An Enhanced Method for Quantifying Simulator Sickness,” International Journal of Aviation Psychology, Vol. 3, No. 3, 1993, pp. 203–220. https://doi.org/10.1207/s15327108ijap0303_3 CrossrefGoogle Scholar

  • [28] Shneiderman B. and Plaisant C., Designing the User Interface: Strategies for Effective Human-Computer Interaction, 5th ed., Addison Wesley Longman, Reading, MA, 2009, Chap. 14. Google Scholar

  • [29] Shefelbine S., Clarkson J., Farmer R. and Eason S., Good Design Practice for Medical Devices and Equipment–Requirements Capture, Univ. of Cambridge, Cambridge, U.K., 2002, https://www-edc.eng.cam.ac.uk/downloads/gooddesignpractice2.pdf. Google Scholar

  • [30] Wegman E. J. and Luo Q., “High Dimensional Clustering Using Parallel Coordinates and the Grand Tour,” Classification and Knowledge Organization, edited by Klar R. and Opitz O., Springer-Verlag, Berlin, 1997, pp. 93–101. Google Scholar

  • [31] Heinrich J. and Weiskopf D., “Parallel Coordinates for Multidimensional Data Visualization: Basic Concepts,” Computing in Science Engineering, Vol. 17, No. 3, 2015, pp. 70–76. https://doi.org/10.1109/MCSE.2015.55 CrossrefGoogle Scholar

  • [32] Artero A. O., Oliveira M. C. F. d. and Levkowitz H., “Uncovering Clusters in Crowded Parallel Coordinates Visualizations,” IEEE Symposium on Information Visualization, IEEE Computer Soc. Press, Washington, D.C., 2004, pp. 81–88. https://doi.org/10.1109/INFVIS.2004.68 Google Scholar

  • [33] Carroll J. M. and Carrithers C., “Training Wheels in a User Interface,” Communications of the ACM, Vol. 27, No. 8, 1984, pp. 800–806. CrossrefGoogle Scholar

  • [34] Nielsen J., “Usability Inspection Methods,” Conference Companion on Human Factors in Computing Systems, Assoc. for Computing Machinery, New York, 1994, pp. 413–414. https://doi.org/10.1145/259963.260531 Google Scholar

  • [35] Nielsen J., “Enhancing the Explanatory Power of Usability Heuristics,” Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Assoc. for Computing Machinery, New York, 1994, pp. 152–158. https://doi.org/10.1145/191666.191729 Google Scholar

  • [36] Grossman T. and Balakrishnan R., “The Bubble Cursor: Enhancing Target Acquisition by Dynamic Resizing of the Cursor’s Activation Area,” Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Assoc. for Computing Machinery, New York, 2005, pp. 281–290. https://doi.org/10.1145/1054972.1055012 Google Scholar

  • [37] Ankerst M., Breunig M. M., Kriegel H.-P. and Sander J., “OPTICS: Ordering Points to Identify the Clustering Structure,” SIGMOD Record, Vol. 28, No. 2, 1999, pp. 49–60. https://doi.org/10.1145/304181.304187 CrossrefGoogle Scholar

  • [38] Ester M., Kriegel H.-P., Sander J. and Xu X., “A Density-Based Algorithm for Discovering Clusters a Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise,” Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, Assoc. for the Advancement of Artificial Intelligence, AAAI Press, 1996, pp. 226–231. Google Scholar

  • [39] Tory M. and Moller T., “Human Factors in Visualization Research,” IEEE Transactions on Visualization and Computer Graphics, Vol. 10, No. 1, 2004, pp. 72–84. CrossrefGoogle Scholar

  • [40] Van Wijk J. J., “Views on Visualization,” IEEE Transactions on Visualization and Computer Graphics, Vol. 12, No. 4, 2006, pp. 421–432. Google Scholar

  • [41] Kristensson P. O., Dahlback N., Anundi D., Bjornstad M., Gillberg H., Haraldsson J., Martensson I., Nordvall M. and Stahl J., “An Evaluation of Space Time Cube Representation of Spatiotemporal Patterns,” IEEE Transactions on Visualization and Computer Graphics, Vol. 15, No. 4, 2009, pp. 696–702. CrossrefGoogle Scholar

  • [42] Ribeiro M. T., Singh S. and Guestrin C., “Why Should I Trust You?: Explaining the Predictions of Any Classifier,” Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’16), Assoc. for Computing Machinery, New York, 2016, pp. 1135–1144. https://doi.org/10.1145/2939672.2939778 Google Scholar

  • [43] Lundberg S. M. and Lee S.-I., “A Unified Approach to Interpreting Model Predictions,” Advances in Neural Information Processing Systems 30, edited by Guyon I., Luxburg U. V., Bengio S., Wallach H., Fergus R., Vishwanathan S. and Garnett R., Curran Associates, 2017, pp. 4765–4774. Google Scholar

  • [44] Ribeiro M. T., Singh S. and Guestrin C., “Anchors: High-Precision Model-Agnostic Explanations,” Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32, No. 1, April 2018, pp. 1527–1535, https://ojs.aaai.org/index.php/AAAI/article/view/11491. Google Scholar

  • [45] Hettenhausen J., Lewis A., Randall M. and Kipouros T., “Interactive Multi-Objective Particle Swarm Optimisation Using Decision Space Interaction,” 2013 IEEE Congress on Evolutionary Computation, IEEE Computer Soc. Press, Washington, D.C., 2013, pp. 3411–3418. https://doi.org/10.1109/CEC.2013.6557988 Google Scholar