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Path-Colored Flow Diagrams: Increasing Business Process Insights by Visualizing Event Logs

  • Koen Daenen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10445)

Abstract

Event logs of a self-care troubleshooting portal indicate that most customers do not follow the directions up to a conclusive end. Consequently, customers risk losing confidence in the support channel, which undermines the competitive strength of the business. We present a method for visual analysis of the event logs that employs graph reduction, and the use of path classification to create a novel type of flow diagram. These diagrams help to discover and communicate new insights, such as important trends about the way the customer traverses through the underlying business process.

Keywords

Workflow analysis Graph visualization Business process insights Troubleshooting process 

References

  1. 1.
    Basole, R.C., Park, H., Gupta, M., Braunstein, M.L., Chau, D.H., Thompson, M.: A visual analytics approach to understanding care process variation and conformance. In: Proceedings of the 2015 Workshop on Visual Analytics in Healthcare, VAHC 2015, NY, USA, New York, pp. 6:1–6:8. ACM (2015). doi: 10.1145/2836034.2836040
  2. 2.
    Bobrik, R., Bauer, T., Reichert, M.: Proviado – personalized and configurable visualizations of business processes. In: Bauknecht, K., Pröll, B., Werthner, H. (eds.) EC-Web 2006. LNCS, vol. 4082, pp. 61–71. Springer, Heidelberg (2006). doi: 10.1007/11823865_7CrossRefGoogle Scholar
  3. 3.
    Bos, K., Hasper, W.: Enabling measuring of the patient flow in an orthopaedic clinic. B.S. thesis, University of Twente (2016). http://essay.utwente.nl/70229/
  4. 4.
    Burkhard, R., Meier, M.: Tube map: evaluation of a visual metaphor for interfunctional communication of complex projects. In: Proceedings of I-Know 2004, vol. 4, pp. 449–456 (2004)Google Scholar
  5. 5.
    Elber, G., Lee, I.K., Kim, M.S.: Comparing offset curve approximation methods. IEEE Comput. Graph. Appl. 17(3), 62–71 (1997). doi: 10.1109/38.586019CrossRefGoogle Scholar
  6. 6.
    Fill, H.G., Höfferer, P.: Visual enhancements of enterprise models. In: Multikonferenz Wirtschaftsinformatik, pp. 541–550 (2006)Google Scholar
  7. 7.
    Hu, F.H., Jiang, J., Wu, X.N., Ru, F.: Reduction rules of graphical representation in a workflow process model. Adv. Sci. Eng. II Appl. Mech. Mater. 135, 709–714 (2012). doi: 10.4028/www.scientific.net/AMM.135-136.709. Trans Tech PublicationsCrossRefGoogle Scholar
  8. 8.
    Hu, J., Perer, A., Wang, F.: Data driven analytics for personalized healthcare. In: Weaver, C.A., Ball, M.J., Kim, G.R., Kiel, J.M. (eds.) Healthcare Information Management Systems. HI, pp. 529–554. Springer, Cham (2016). doi: 10.1007/978-3-319-20765-0_31CrossRefGoogle Scholar
  9. 9.
    Keim, D., Andrienko, G., Fekete, J.-D., Görg, C., Kohlhammer, J., Melançon, G.: Visual analytics: definition, process, and challenges. In: Kerren, A., Stasko, J.T., Fekete, J.-D., North, C. (eds.) Information Visualization. LNCS, vol. 4950, pp. 154–175. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-70956-5_7CrossRefGoogle Scholar
  10. 10.
    Kriglstein, S., Wallner, G., Rinderle-Ma, S.: A visualization approach for difference analysis of process models and instance traffic. In: Daniel, F., Wang, J., Weber, B. (eds.) BPM 2013. LNCS, vol. 8094, pp. 219–226. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-40176-3_18CrossRefGoogle Scholar
  11. 11.
    McCabe, T.J.: A complexity measure. IEEE Trans. Softw. Eng. SE 2(4), 308–320 (1976). doi: 10.1109/TSE.1976.233837MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Reichert, M.: Visualizing large business process models: challenges, techniques, applications. In: Rosa, M., Soffer, P. (eds.) BPM 2012. LNBIP, vol. 132, pp. 725–736. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-36285-9_73CrossRefGoogle Scholar
  13. 13.
    Riehmann, P., Hanfler, M., Froehlich, B.: Interactive sankey diagrams. In: IEEE Symposium on Information Visualization, INFOVIS 2005, pp. 233–240 (2005). doi: 10.1109/INFVIS.2005.1532152
  14. 14.
    Sadiq, W., Orlowska, M.E.: Applying graph reduction techniques for identifying structural conflicts in process models. In: Jarke, M., Oberweis, A. (eds.) CAiSE 1999. LNCS, vol. 1626, pp. 195–209. Springer, Heidelberg (1999). doi: 10.1007/3-540-48738-7_15CrossRefGoogle Scholar
  15. 15.
    Sadiq, W., Orlowska, M.E.: Analyzing process models using graph reduction techniques. Inf. Syst. 25(2), 117–134 (2000). doi: 10.1016/S0306-4379(00)00012-0. http://www.sciencedirect.com/science/article/pii/S0306437900000120CrossRefGoogle Scholar
  16. 16.
    Schmidt, M.: The sankey diagram in energy and material flow management. J. Ind. Ecol. 12(1), 82–94 (2008). doi: 10.1111/j.1530-9290.2008.00004.xCrossRefGoogle Scholar
  17. 17.
    Tanahashi, Y., Ma, K.L.: Design considerations for optimizing storyline visualizations. IEEE Trans. Vis. Comput. Graph. 18(12), 2679–2688 (2012). doi: 10.1109/TVCG.2012.212CrossRefGoogle Scholar
  18. 18.
    Verbeek, H., Wynn, M., van der Aalst, W., ter Hofstede, A.: Reduction rules for reset/inhibitor nets. J. Comput. Syst. Sci. 76(2), 125–143 (2010). doi: 10.1016/j.jcss.2009.06.003MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Vogelgesang, T., Appelrath, H.-J.: PMCube: a data-warehouse-based approach for multidimensional process mining. In: Reichert, M., Reijers, H.A. (eds.) BPM 2015. LNBIP, vol. 256, pp. 167–178. Springer, Cham (2016). doi: 10.1007/978-3-319-42887-1_14CrossRefGoogle Scholar
  20. 20.
    Wongsuphasawat, K., Gotz, D.: Exploring flow, factors, and outcomes of temporal event sequences with the outflow visualization. IEEE Trans. Vis. Comput. Graph. 18(12), 2659–2668 (2012). doi: 10.1109/TVCG.2012.225CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Nokia Bell LabsAntwerpBelgium

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