Enabling Interactive Process Analysis with Process Mining and Visual Analytics

P. M. Dixit, H. S. Garcia Caballero, A. Corvò, B. F. A. Hompes, J. C. A. M. Buijs, W. M. P. van der Aalst

2017

Abstract

In a typical healthcare setting, specific clinical care pathways can be defined by the hospitals. Process mining provides a way of analyzing the care pathways by analyzing the event data extracted from the hospital information systems. Process mining can be used to optimize the overall care pathway, and gain interesting insights into the actual execution of the process, as well as to compare the expectations versus the reality. In this paper, a generic novel tool called InterPretA, is introduced which builds upon pre-existing process mining and visual analytics techniques to enable the user to perform such process oriented analysis. InterPretA contains a set of options to provide high level conformance analysis of a process from different perspectives. Furthermore, InterPretA enables detailed investigative analysis by letting the user interactively analyze, visualize and explore the execution of the processes from the data perspective.

References

  1. Aalst, W. M. P. v. d. (2016). Process Mining - Data Science in Action, Second Edition. Springer.
  2. Aalst, W. M. P. v. d., Leoni, d. M., and ter Hofstede, A. H. (2011). Process mining and visual analytics: Breathing life into business process models.
  3. Adriansyah, A., Dongen, B. F. v., and Aalst, W. M. P. v. d. (2011). Towards robust conformance checking. In Business Process Management Workshops, volume 66 of Lecture Notes in Business Information Processing, pages 122-133. Springer Berlin Heidelberg.
  4. Buffett, S. and Geng, L. (2010). Using classification methods to label tasks in process mining. Journal of Software Maintenance and Evolution: Research and Practice, 22(6-7):497-517.
  5. Dongen, B. F. v., de Medeiros, A. K. A., Verbeek, H. M. W., Weijters, A. J. M. M., and Aalst, W. M. P. v. d. (2005). The prom framework: A new era in process mining tool support. In International Conference on Application and Theory of Petri Nets, pages 444-454. Springer.
  6. Dwivedi, A., Bali, R., James, A., and Naguib, R. (2001). Workflow management systems: the healthcare technology of the future? In Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE, volume 4, pages 3887-3890. IEEE.
  7. Fernandez-Llatas, C., Martinez-Millana, A., MartinezRomero, A., Bened, J. M., and Traver, V. (2015). Diabetes care related process modelling using process mining techniques. lessons learned in the application of interactive pattern recognition: coping with the spaghetti effect. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pages 2127-2130.
  8. Goedertier, S., Martens, D., Baesens, B., Haesen, R., and Vanthienen, J. (2007). Process mining as first-order classification learning on logs with negative events. In International Conference on Business Process Management, pages 42-53. Springer.
  9. Graeber, S. (1997). The impact of workflow management systems on the design of hospital information systems. In Proceedings of the AMIA Annual Fall Symposium, page 856. American Medical Informatics Association.
  10. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I. H. (2009). The WEKA data mining software: an update. SIGKDD Explor. Newsl., 11(1):10-18.
  11. Hompes, B. F. A., Buijs, J. C. A. M., and Aalst, W. M. P. v. d. (2016). A Generic Framework for ContextAware Process Performance Analysis, pages 300-317. Springer International Publishing, Cham.
  12. Knuplesch, D., Reichert, M., Ly, L. T., Kumar, A., and Rinderle-Ma, S. (2013). Visual Modeling of Business Process Compliance Rules with the Support of Multiple Perspectives, pages 106-120. Springer Berlin Heidelberg, Berlin, Heidelberg.
  13. Kriglstein, S., Pohl, M., Rinderle-Ma, S., and Stallinger, M. (2016). Visual Analytics in Process Mining: Classi-fi cation of Process Mining Techniques. In Andrienko, N. and Sedlmair, M., editors, EuroVis Workshop on Visual Analytics (EuroVA). The Eurographics Association.
  14. Leemans, S. J. J., F. D. and Aalst, W. M. P. v. d. (2014). Discovering block-structured process models from event logs containing infrequent behaviour. In Business Process Management Workshops, pages 66-78. Springer.
  15. Leemans, S., Fahland, D., and Aalst, W. M. P. v. d. (2014). Process and deviation exploration with inductive visual miner.
  16. Leoni, M. d., Maggi, F., and Aalst, W. M. P. v. d. (2015). An alignment-based framework to check the conformance of declarative process models and to preprocess eventlog data. Information Systems, 47:258 - 277.
  17. Mahaffey, S. (2004). Optimizing patient flow in the enterprise. Health management technology, 25(8):34-37.
  18. Mannhardt, F., de Leoni, M., and Reijers, H. (2015). The multi-perspective process explorer. In Proceedings of the BPM Demo Session 2015, Co-located with the 13th International Conference on Business Process Management {(BPM} 2015), Innsbruck, Austria, September 2, 2015, pages 130-134. CEUR Workshop Proceedings.
  19. Munoz-Gama, J., Carmona, J., and Aalst, W. M. P. v. d. (2014). Single-entry single-exit decomposed conformance checking. Information Systems, 46.
  20. Poggi, N., Muthusamy, V., Carrera, D., and Khalaf, R. (2013). Business process mining from e-commerce web logs. In Business Process Management, pages 65-80. Springer.
  21. Ramezani Taghiabadi, E., Fahland, D., Dongen, B. F. v., and Aalst, W. M. P. v. d. (2013). Diagnostic Information for Compliance Checking of Temporal Compliance Requirements, pages 304-320. Springer Berlin Heidelberg, Berlin, Heidelberg.
  22. Schulz, H.-J., Nocke, T., Heitzler, M., and Schumann, H. (2013). A design space of visualization tasks. IEEE Transactions on Visualization and Computer Graphics, 19(12):2366-2375.
  23. Sutherland, J. and van den Heuvel, W. (2006). Towards an intelligent hospital environment: Adaptive workflow in the or of the future. In Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06), volume 5, pages 100b-100b.
  24. Weigl, M., M üller, A., Vincent, C., Angerer, P., and Sevdalis, N. (2012). The association of workflow interruptions and hospital doctors' workload: a prospective observational study. BMJ quality & safety, 21(5):399- 407.
Download


Paper Citation


in Harvard Style

M. Dixit P., S. Garcia Caballero H., Corvò A., F. A. Hompes B., C. A. M. Buijs J. and M. P. van der Aalst W. (2017). Enabling Interactive Process Analysis with Process Mining and Visual Analytics . In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: ACP, (BIOSTEC 2017) ISBN 978-989-758-213-4, pages 573-584. DOI: 10.5220/0006272605730584


in Bibtex Style

@conference{acp17,
author={P. M. Dixit and H. S. Garcia Caballero and A. Corvò and B. F. A. Hompes and J. C. A. M. Buijs and W. M. P. van der Aalst},
title={Enabling Interactive Process Analysis with Process Mining and Visual Analytics},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: ACP, (BIOSTEC 2017)},
year={2017},
pages={573-584},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006272605730584},
isbn={978-989-758-213-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: ACP, (BIOSTEC 2017)
TI - Enabling Interactive Process Analysis with Process Mining and Visual Analytics
SN - 978-989-758-213-4
AU - M. Dixit P.
AU - S. Garcia Caballero H.
AU - Corvò A.
AU - F. A. Hompes B.
AU - C. A. M. Buijs J.
AU - M. P. van der Aalst W.
PY - 2017
SP - 573
EP - 584
DO - 10.5220/0006272605730584