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Visual Data Science for Industrial Applications

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Digital Transformation

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Abstract

Advances in sensor and data acquisition technology and in methods of data analysis pose many research challenges but also promising application opportunities in many domains. The need to cope with and leverage large sensor data streams is particularly urgent for industrial applications due to strong business competition and innovation pressure. In maintenance, for example, sensor readings of machinery or products may allow to predict at which point in time maintenance will be required and allow to schedule service operations respectively. Another application is the discovery of the relationships between production input parameters on the quality of the output products. Analysis of respective industrial data typically cannot be done in an out-of-the-box manner but requires to incorporate background knowledge from fields such as engineering, operation research, and business to be effective. Hence, approaches for interactive and visual data analysis can be particularly useful for analyzing complex industrial data, combining the advantages of modern automatic data analysis with domain knowledge and hypothesis generation capabilities of domain experts.

In this chapter, we introduce some of the main principles of visual data analysis. We discuss how techniques for data visualization, data analysis, and user interaction can be combined to analyze data, generate and verify hypotheses about patterns in data, and present the findings. We discuss this in the light of important requirements and applications in the analysis of industrial data and based on current research in the area. We provide examples for visual data analysis approaches, including condition monitoring, quality control, and production planning.

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Notes

  1. 1.

    https://www.tableau.com/

  2. 2.

    https://powerbi.microsoft.com/

  3. 3.

    https://spotfire.tibco.com/

  4. 4.

    https://jupyter.org/

  5. 5.

    https://rmarkdown.rstudio.com/

  6. 6.

    https://vega.github.io/vega/

  7. 7.

    https://vega.github.io/vega-lite/

  8. 8.

    https://www.chartjs.org/

  9. 9.

    https://plotly.com/

  10. 10.

    https://d3js.org/

References

  1. Abate, A., Guida, M., Leoncini, P., Nappi, M., Ricciardi, S.: Ahaptic-based approach to virtual training for aerospace industry. Journal of Visual Languages & Computing 20, 318–325 (2009). https://doi.org/10.1016/j.jvlc.2009.07.003

  2. Aigner, W., Miksch, S., Schumann, H., Tominski, C.: Visualization of Time-Oriented Data. Human-Computer Interaction Series, Springer (2011). https://doi.org/10.1007/978-0-85729-079-3

  3. Andrienko, G.L., Andrienko, N.V., Drucker, S.M., Fekete, J., Fisher, D., Idreos, S., Kraska, T., Li, G., Ma, K., Mackinlay, J.D., Oulasvirta, A., Schreck, T., Schumann, H., Stonebraker, M., Auber, D., Bikakis, N., Chrysanthis, P.K., Papastefanatos, G., Sharaf, M.A.: Big data visualization and analytics: Future research challenges and emerging applications. In: Proceedings of the Workshops of the EDBT/ICDT 2020 Joint Conference (2020), http://ceur-ws.org/Vol-2578/BigVis1.pdf

  4. Beck, F., Burch, M., Diehl, S., Weiskopf, D.: A taxonomy and survey of dynamic graph visualization. Comput. Graph. Forum 36(1), 133–159 (2017). https://doi.org/10.1111/cgf.12791

  5. Behrisch, M., Korkmaz, F., Shao, L., Schreck, T.: Feedback-driven interactive exploration of large multidimensional data supported by visual classifier. In: 2014 IEEE Conference on Visual Analytics Science and Technology (VAST). pp. 43–52 (2014). https://doi.org/10.1109/VAST.2014.7042480

  6. Behrisch, M., Bach, B., Riche, N.H., Schreck, T., Fekete, J.: Matrix reordering methods for table and network visualization. Computer Graphics Forum 35(3), 693–716 (2016). https://doi.org/10.1111/cgf.12935

  7. Behrisch, M., Streeb, D., Stoffel, F., Seebacher, D., Matejek, B., Weber, S.H., Mittelstädt, S., Pfister, H., Keim, D.A.: Commercial visual analytics systems-advances in the big data analytics field. IEEE Trans. Vis. Comput. Graph. 25(10), 3011–3031 (2019). https://doi.org/10.1109/TVCG.2018.2859973

  8. Bertin, J., Berg, W., Wainer, H., of Wisconsin Press, U.: Semiology of Graphics. University of Wisconsin Press (1983), https://books.google.at/books?id=luZQAAAAMAAJ

  9. Borgo, R., Chen, M., Daubney, B., Grundy, E., Heidemann, G., Höferlin, B., Höferlin, M., Leitte, H., Weiskopf, D., Xie, X.: State of the art report on video-based graphics and video visualization. Comput. Graph. Forum 31(8), 2450–2477 (2012). https://doi.org/10.1111/j.1467-8659.2012.03158.x

  10. Bouali, F., Guettala, A., Venturini, G.: Vizassist: An interactive user assistant for visual data mining. Vis. Comput. 32(11), 1447–1463 (Nov 2016). https://doi.org/10.1007/s00371-015-1132-9

  11. Canizo, M., Onieva, E., Conde, A., Charramendieta, S., Trujillo, S.: Real-time predictive maintenance for wind turbines using big data frameworks. In: 2017 IEEE International Conference on Prognostics and Health Management (ICPHM). pp. 70–77 (2017)

    Google Scholar 

  12. Cao, L.: Data science: A comprehensive overview. ACM Comput. Surv. 50(3) (2017). https://doi.org/10.1145/3076253

  13. Card, S., Mackinlay, J., Shneiderman, B.: Readings in information visualization: using vision to think. Morgan Kaufmann Publishers Inc. (1999)

    Google Scholar 

  14. Ceneda, D., Gschwandtner, T., May, T., Miksch, S., Schulz, H., Streit, M., Tominski, C.: Characterizing guidance in visual analytics. IEEE Trans. Vis. Comput. Graph. 23(1), 111–120 (2017). https://doi.org/10.1109/TVCG.2016.2598468

  15. Ceneda, D., Gschwandtner, T., Miksch, S.: A review of guidance approaches in visual data analysis: A multifocal perspective. Comput. Graph. Forum 38(3), 861–879 (2019). https://doi.org/10.1111/cgf.13730

  16. Cibulski, L., Mitterhofer, H., May, T., Kohlhammer, J.: PAVED: Pareto Front Visualization for Engineering Design. Computer Graphics Forum (2020). https://doi.org/10.1111/cgf.13990

  17. Dutta, S., Shen, H., Chen, J.: In situ prediction driven feature analysis in jet engine simulations. In: 2018 IEEE Pacific Visualization Symposium (PacificVis). pp. 66–75 (2018)

    Google Scholar 

  18. Eirich, J., Bonart, J., Jackle, D., Sedlmair, M., Schmid, U., Fischbach, K., Schreck, T., Bernard, J.: Irvine: A design study on analyzing correlation patterns of electrical engines. IEEE Transactions on Visualization & Computer Graphics (01),  1–1 (sep 2021). https://doi.org/10.1109/TVCG.2021.3114797

  19. Endert, A., Ribarsky, W., Turkay, C., Wong, B.L.W., Nabney, I.T., Blanco, I.D., Rossi, F.: The state of the art in integrating machine learning into visual analytics. CoRR abs/1802.07954 (2018), http://arxiv.org/abs/1802.07954

  20. Froese, M., Tory, M.: Lessons learned from designing visualization dashboards. IEEE Computer Graphics and Applications 36(2), 83–89 (2016). https://doi.org/10.1109/MCG.2016.33

  21. Guo, R., Cheng, L., Li, J., Hahn, P.R., Liu, H.: A survey of learning causality with data: Problems and methods. ACM Computing Surveys (CSUR) (2018)

    Google Scholar 

  22. Han, J., Kamber, M., Pei, J.: Data mining concepts and techniques, third edition (2012)

    Google Scholar 

  23. Heer, J., Bostock, M.: Crowdsourcing graphical perception: Using mechanical turk to assess visualization design. p. 203-212. CHI ’10, Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1753326.1753357

  24. Hohman, F., Kahng, M., Pienta, R., Chau, D.H.: Visual analytics in deep learning: An interrogative survey for the next frontiers. CoRR abs/1801.06889 (2018), http://arxiv.org/abs/1801.06889

  25. Holst, A., Pashami, S., Bae, J.: Incremental causal discovery and visualization. In: Proceedings of the Workshop on Interactive Data Mining. WIDM’19, Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3304079.3310287

  26. Janetzko, H., Stoffel, F., Mittelstädt, S., Keim, D.A.: Anomaly detection for visual analytics of power consumption data. Computers & Graphics 38, 27–37 (2014). https://doi.org/10.1016/j.cag.2013.10.006, http://www.sciencedirect.com/science/article/pii/S0097849313001477

  27. Jänicke, S., Franzini, G., Cheema, M.F., Scheuermann, G.: Visual text analysis in digital humanities. Comput. Graph. Forum 36(6), 226–250 (2017). https://doi.org/10.1111/cgf.12873

  28. Jekic, N., Mutlu, B., Faschang, M., Neubert, S., Thalmann, S., Schreck, T.: Visual analysis of aluminum production data with tightly linked views. In: 21st Eurographics Conference on Visualization, EuroVis 2019 - Posters, Porto, Portugal, June 3–7, 2019. pp. 49–51 (2019). https://doi.org/10.2312/eurp.20191143

  29. Jo, J., Huh, J., Park, J., Kim, B., Seo, J.: Livegantt: Interactively visualizing a large manufacturing schedule. IEEE Transactions on Visualization and Computer Graphics 20(12), 2329–2338 (2014)

    Google Scholar 

  30. Keim, D., Kohlhammer, J., Ellis, G., Mansmann, F. (eds.): Mastering the information age : solving problems with visual analytics. Goslar: Eurographics Association (2010), https://diglib.eg.org/handle/10.2312/14803

  31. von Landesberger, T., Kuijper, A., Schreck, T., Kohlhammer, J., van Wijk, J.J., Fekete, J., Fellner, D.W.: Visual analysis of large graphs: State-of-the-art and future research challenges. Computer Graphics Forum 30(6), 1719–1749 (2011). https://doi.org/10.1111/j.1467-8659.2011.01898.x

  32. Laney, D.: 3-D Data Management: Controlling Data Volume. Velocity and Variety, META Group Original Research Note (2001)

    Google Scholar 

  33. Lu, Y., Garcia, R., Hansen, B., Gleicher, M., Maciejewski, R.: The state-of-the-art in predictive visual analytics. Comput. Graph. Forum 36(3), 539–562 (2017). https://doi.org/10.1111/cgf.13210

  34. Mackinlay, J.: Automating the design of graphical presentations of relational information. ACM Transactions on Graphics 5(2), 110–141 (Apr 1986)

    Google Scholar 

  35. Maier, A., Tack, T., Niggemann, O.: Visual anomaly detection in production plants. In: Ferrier, J., Bernard, A., Gusikhin, O.Y., Madani, K. (eds.) ICINCO 2012 – Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics, Volume 1, Rome, Italy, 28–31 July, 2012. pp. 67–75. SciTePress (2012)

    Google Scholar 

  36. Munzner, T.: Visualization Analysis and Design. CRC Press (2014)

    Google Scholar 

  37. Mutlu, B., Gashi, M., Sabol, V.: Towards a task-based guidance in exploratory visual analytics. In: 54th Hawaii International Conference on System Sciences, HICSS 2021, Kauai, Hawaii, USA, January 5, 2021. pp. 1–9. ScholarSpace (2021), http://hdl.handle.net/10125/70789

  38. Mutlu, B., Veas, E., Trattner, C.: Vizrec: Recommending personalized visualizations. ACM Transactions on Interactive Intelligent Systems 6(4), 31:1–31:39 (2016)

    Google Scholar 

  39. Nara, A.: Visual analytics of movement, by gennady andrienko, natalia andrienko, peter bak, daniel keim and stefan wrobel, berlin heidelberg, springer-verlag, 2013, xviii + 387 pp., us\$129 (hardcover), ISBN 978-3-642-37582-8. Ann. GIS 21(1), 91–92 (2015). https://doi.org/10.1080/19475683.2015.992828

  40. Nobre, C., Meyer, M.D., Streit, M., Lex, A.: The state of the art in visualizing multivariate networks. Comput. Graph. Forum 38(3), 807–832 (2019). https://doi.org/10.1111/cgf.13728

  41. Peng, G., Hou, X., Gao, J., Cheng, D.: A visualization system for integrating maintainability design and evaluation at product design stage. The International Journal of Advanced Manufacturing Technology 61 (2011). https://doi.org/10.1007/s00170-011-3702-y

  42. Post, T., Ilsen, R., Hamann, B., Hagen, H., Aurich, J.C.: User-Guided Visual Analysis of Cyber-Physical Production Systems. Journal of Computing and Information Science in Engineering 17(2) (2017)

    Google Scholar 

  43. Roberts, J.C.: State of the art: Coordinated multiple views in exploratory visualization. In: Fifth International Conference on Coordinated and Multiple Views in Exploratory Visualization (CMV 2007). pp. 61–71 (2007)

    Google Scholar 

  44. Sacha, D., Stoffel, A., Stoffel, F., Kwon, B.C., Ellis, G., Keim, D.A.: Knowledge generation model for visual analytics. IEEE Transactions on Visualization and Computer Graphics 20, 1604–1613 (2014)

    Google Scholar 

  45. Sakai, R.: Biological data visualization: Analysis and design (2016)

    Google Scholar 

  46. Sarikaya, A., Correll, M., Bartram, L., Tory, M., Fisher, D.: What do we talk about when we talk about dashboards? IEEE Transactions on Visualization and Computer Graphics 25(1), 682–692 (2019)

    Google Scholar 

  47. Sedlmair, M., Isenberg, P., Baur, D., Mauerer, M., Pigorsch, C., Butz, A.: Cardiogram: Visual analytics for automotive engineers. pp. 1727–1736 (2011). https://doi.org/10.1145/1978942.1979194

  48. Shao, L., Silva, N., Eggeling, E., Schreck, T.: Visual exploration of large scatter plot matrices by pattern recommendation based on eye tracking. In: Proceedings of the 2017 ACM Workshop on Exploratory Search and Interactive Data Analytics. pp. 9–16. ESIDA ’17, ACM, New York, NY, USA (2017). https://doi.org/10.1145/3038462.3038463

  49. Shneiderman, B.: The eyes have it: a task by data type taxonomy for information visualizations. In: Proc. IEEE Symposium on Visual Languages. pp. 336–343. IEEE (1996)

    Google Scholar 

  50. Silva, N., Schreck, T., Veas, E., Sabol, V., Eggeling, E., Fellner, D.W.: Leveraging eye-gaze and time-series features to predict user interests and build a recommendation model for visual analysis. In: Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications. pp. 13:1–13:9. ETRA ’18, ACM, New York, NY, USA (2018). https://doi.org/10.1145/3204493.3204546

  51. Steichen, B., Carenini, G., Conati, C.: User-adaptive information visualization: Using eye gaze data to infer visualization tasks and user cognitive abilities. In: Proceedings of the 2013 International Conference on Intelligent User Interfaces. p. 317–328. IUI ’13, Association for Computing Machinery, New York, NY, USA (2013). https://doi.org/10.1145/2449396.2449439

  52. Suschnigg, J., Mutlu, B., Koutroulis, G., Sabol, V., Thalmann, S., Schreck, T.: Visual exploration of anomalies in cyclic time series data with matrix and glyph representations. Big Data Research 26, 100251 (2021). https://doi.org/10.1016/j.bdr.2021.100251

  53. Thalmann, S., Mangler, J., Schreck, T., Huemer, C., Streit, M., Pauker, F., Weichhart, G., Schulte, S., Kittl, C., Pollak, C., Vukovic, M., Kappel, G., Gashi, M., Rinderle-Ma, S., Suschnigg, J., Jekic, N., Lindstaedt, S.: Data analytics for industrial process improvement – a vision paper. In: IEEE 20th Conference on Business Informatics (CBI). vol. 02, pp. 92–96 (2018)

    Google Scholar 

  54. Thomas, J.J., Cook, K.A.: Illuminating the Path: The Research and Development Agenda for Visual Analytics. National Visualization and Analytics Ctr (2005)

    Google Scholar 

  55. Tominski, C., Schuman, H.: Interactive Visual Data Analysis. AK Peters/CRC Press (2020), forthcoming

    Google Scholar 

  56. Wang, J., Mueller, K.: Visual causality analysis made practical. In: 2017 IEEE Conference on Visual Analytics Science and Technology (VAST). pp. 151–161 (2017)

    Google Scholar 

  57. Ward, M., Grinstein, G., Keim, D.: Interactive Data Visualization: Foundations, Techniques, and Applications. A. K. Peters, Ltd., USA (2010)

    Google Scholar 

  58. Wu, P.Y.F.: Visualizing capacity and load in production planning. In: Proceedings Fifth International Conference on Information Visualisation. pp. 357–360 (2001)

    Google Scholar 

  59. Wu, W., Zheng, Y., Chen, K., Wang, X., Cao, N.: A visual analytics approach for equipment condition monitoring in smart factories of process industry. In: 2018 IEEE Pacific Visualization Symposium (PacificVis). pp. 140–149 (2018)

    Google Scholar 

  60. Wörner, M., Metzger, M., T.Ertl: Dataflow-based Visual Analysis for Fault Diagnosis and Predictive Maintenance in Manufacturing. In: Pohl, M., Schumann, H. (eds.) EuroVis Workshop on Visual Analytics. The Eurographics Association (2013). https://doi.org/10.2312/PE.EuroVAST.EuroVA13.055-059

  61. Xu, F., Uszkoreit, H., Du, Y., Fan, W., Zhao, D., Zhu, J.: Explainable AI: A Brief Survey on History, Research Areas, Approaches and Challenges, pp. 563–574 (2019)

    Google Scholar 

  62. Xu, P., Mei, H., Ren, L., Chen, W.: Vidx: Visual diagnostics of assembly line performance in smart factories. IEEE Transactions on Visualization and Computer Graphics 23(1), 291–300 (2017)

    Google Scholar 

  63. Yen, C., Parameswaran, A., Fu, W.: An exploratory user study of visual causality analysis. Computer Graphics Forum 38, 173–184 (06 2019). https://doi.org/10.1111/cgf.13680

  64. Yi, J.S., ah Kang, Y., Stasko, J.: Toward a deeper understanding of the role of interaction in information visualization. IEEE Trans. Vis. Comput. Graph. 13(6), 1224–1231 (2007)

    Google Scholar 

  65. Zhou, F., Lin, X., Liu, C., Zhao, Y., Xu, P., Ren, L., Xue, T., Ren, L.: A survey of visualization for smart manufacturing. Journal of Visualization 22, 419–435 (2019)

    Google Scholar 

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Acknowledgements

This work has partially been supported by the FFG, Contract No. 881844: “Pro\({}^2\)Future is funded within the Austrian COMET Program Competence Centers for Excellent Technologies under the auspices of the Austrian Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology, the Austrian Federal Ministry for Digital and Economic Affairs and of the Provinces of Upper Austria and Styria. COMET is managed by the Austrian Research Promotion Agency FFG.”

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Authors and Affiliations

  1. Computer Graphics and Knowledge Visualization, TU Graz, Graz, Austria

    Tobias Schreck

  2. Cognitive Decision Support, Pro2Future GmbH, Graz, Austria

    Belgin Mutlu

  3. Visual Data Science Lab, Johannes Kepler University Linz, Linz, Austria

    Marc Streit

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  1. Tobias Schreck
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  2. Belgin Mutlu
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Correspondence to Marc Streit .

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  1. Technische Universität München, Garching b. München, Bayern, Germany

    Birgit Vogel-Heuser

  2. Johannes Kepler University Linz, Linz, Oberösterreich, Austria

    Manuel Wimmer

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Schreck, T., Mutlu, B., Streit, M. (2023). Visual Data Science for Industrial Applications. In: Vogel-Heuser, B., Wimmer, M. (eds) Digital Transformation. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-65004-2_18

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