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Putting Decision Mining into Context: A Literature Study

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Digital Business Transformation

Part of the book series: Lecture Notes in Information Systems and Organisation ((LNISO,volume 38))

Abstract

The value of a decision can be increased through analyzing the decision logic, and the outcomes. The more often a decision is taken, the more data becomes available about the results. More available data results into smarter decisions and increases the value the decision has for an organization. The research field addressing this problem is Decision mining. By conducting a literature study on the current state of Decision mining, we aim to discover the research gaps and where Decision mining can be improved upon. Our findings show that the concepts used in the Decision mining field and related fields are ambiguous and show overlap. Future research directions are discovered to increase the quality and maturity of Decision mining research. This could be achieved by focusing more on Decision mining research, a change is needed from a business process Decision mining approach to a decision focused approach.

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References

  1. Smirnov, A., Pashkin, M., Levashova, T., Kashevnik, A., Shilov, N. (2009). Context-driven decision mining. In Encyclopedia of data warehousing and mining (pp. 320–327). Information Science Reference, Hershey, NY.

    Google Scholar 

  2. van der Aalst, W. M. P.: Process mining: Discovery, conformance and enhancement of business processes. Springer Science & Business Media (2011).

    Google Scholar 

  3. Rozinat, A., van der Aalst, W. M. P.: Decision mining in ProM. In: Dustdar, S., Fiadeiro, J. L., & Sheth, A. P. (Eds.), Business process management: 4th international conference, BPM 2006, Vienna, Austria, 5–7 September 2006. Proceedings (pp. 420–425). Heidelberg, Berlin: Springer. https://doi.org/10.1007/11841760_33.

  4. Arnott, D., & Pervan, G. (2005). A critical analysis of decision support systems research. Journal of Information Technology, 20, 67–87.

    Article  Google Scholar 

  5. Horita, F. E. A., de Albuquerque, J. P., Marchezini, V., & Mendiondo, E. M. (2017). Bridging the gap between decision-making and emerging big data sources: An application of a model-based framework to disaster management in Brazil. Decision Support Systems, 97, 12–22. https://doi.org/10.1016/j.dss.2017.03.001.

    Article  Google Scholar 

  6. Mohemad, R., Hamdan, A. R., Othman, Z. A., & Noor, N. M. M. (2010). Decision support systems (DSS) in construction tendering processes. International Journal of Computer Science Issues, 7, 35–45. https://doi.org/10.1109/ICSSSM.2008.4598482.

    Article  Google Scholar 

  7. Chiang, W. Y. K., Zhang, D., & Zhou, L. (2006). Predicting and explaining patronage behavior toward web and traditional stores using neural networks: A comparative analysis with logistic regression. Decision Support Systems, 41, 514–531. https://doi.org/10.1016/j.dss.2004.08.016.

    Article  Google Scholar 

  8. Li, X.-B. (2005). A scalable decision tree system and its application in pattern recognition and intrusion detection. Decision Support Systems, 41, 112–130. https://doi.org/10.1016/j.dss.2004.06.016.

    Article  Google Scholar 

  9. Thomassey, S., & Fiordaliso, A. (2006). A hybrid sales forecasting system based on clustering and decision trees. Decision Support Systems, 42, 408–421. https://doi.org/10.1016/j.dss.2005.01.008.

    Article  Google Scholar 

  10. De Smedt, J., Vanden Broucke, S. K. L. M., Obregon, J., Kim, A., Jung, J. Y., Vanthienen, J. (2017). Decision mining in a broader context: An overview of the current landscape and future directions. In Lecture notes in business information processing (pp. 197–207). Springer International Publishing. https://doi.org/10.1007/978-3-319-58457-7_15.

  11. Paré, G., Trudel, M. C., Jaana, M., & Kitsiou, S. (2015). Synthesizing information systems knowledge: a typology of literature reviews. Information & Management, 52, 183–199. https://doi.org/10.1016/j.im.2014.08.008.

    Article  Google Scholar 

  12. Edmondson, A. C., & Mcmanus, S. E. (2007). Methodological fit in management field research. Academy of Management Review, 32, 1155–1179.

    Article  Google Scholar 

  13. Hirsch, J. E. (2010). An index to quantify an individual’s scientific research output that takes into account the effect of multiple coauthorship. Scientometrics, 85, 741–754. https://doi.org/10.1007/s11192-010-0193-9.

    Article  Google Scholar 

  14. Baumeister, R. F., & Leary, M. R. (1997). Writing narrative literature reviews. Review of General Psychology, 1, 311–320. https://doi.org/10.1037/1089-2680.1.3.311.

    Article  Google Scholar 

  15. Webster, J., Watson, R. T. (2002). Analyzing the past to prepare for the future: Writing a literature review. MIS Quarterly, 26, xiii–xxiii. https://doi.org/10.1.1.104.6570.

    Google Scholar 

  16. Harzing, A. -W., Alakangas, S. (2016). Google scholar, scopus and the web of science: A longitudinal and cross-disciplinary comparison. Scientometrics 106, 787–804 (2016). https://doi.org/10.1007/s11192-015-1798-9.

  17. Wildgaard, L. (2015). A comparison of 17 author-level bibliometric indicators for researchers in astronomy, environmental science, philosophy and public health in web of science and google scholar. Scientometrics, 104, 873–906. https://doi.org/10.1007/s11192-015-1608-4.

    Article  Google Scholar 

  18. Franceschet, M. (2010). A comparison of bibliometric indicators for computer science scholars and journals on Web of Science and Google Scholar. Scientometrics, 83, 243–258. https://doi.org/10.1007/s11192-009-0021-2.

    Article  Google Scholar 

  19. Amara, N., & Landry, R. (2012). Counting citations in the field of business and management: Why use Google scholar rather than the web of science. Scientometrics, 93, 553–581. https://doi.org/10.1007/s11192-012-0729-2.

    Article  Google Scholar 

  20. Gehanno, J.-F., Rollin, L., & Darmoni, S. (2013). Is the coverage of google scholar enough to be used alone for systematic reviews. BMC medical informatics and decision making, 13, 7. https://doi.org/10.1186/1472-6947-13-7.

    Article  Google Scholar 

  21. Parmesan, C., & Yohe, G. (2003). A globally coherent fingerprint of climate change impacts across natural systems. Nature, 421, 37–42. https://doi.org/10.1038/nature01286.

    Article  Google Scholar 

  22. Lek, M., Karczewski, K. J., Minikel, E. V., Samocha, K. E., Banks, E., Fennell, T., et al. (2016). Analysis of protein-coding genetic variation in 60,706 humans. Nature, 536, 285–291. https://doi.org/10.1038/nature19057.

    Article  Google Scholar 

  23. Cardinale, B. J., Duffy, J. E., Gonzalez, A., Hooper, D. U., Perrings, C., Venail, P., et al. (2012). Biodiversity loss and its impact on humanity. Nature, 486, 59–67. https://doi.org/10.1038/nature11148.

    Article  Google Scholar 

  24. Levy, Y., Ellis, T.J. (2006). A systems approach to conduct an effective literature review in support of information systems research. Informing Science 9, 181–211. https://doi.org/10.28945/479.

  25. Okoli, C. (2015). A guide to conducting a standalone systematic literature review. Communications of the Association for Information Systems, 37, 879–910. https://doi.org/10.2139/ssrn.1954824.

    Article  Google Scholar 

  26. Vercellis, C.: Business intelligence: Data mining and optimization for decision making. New York: Wiley.

    Google Scholar 

  27. Zikmund, W., Babin, B., Carr, J., Griffin, M. (2009). Business research methods. South-Western.

    Google Scholar 

  28. Chen, H., Chiang, R. H. L., Storey, V. C.: Business intelligence and analytics: From big data to big impact. MIS Quarterly, 1165–1188. https://doi.org/10.2307/41703503.

  29. Loennqvist, A., & Pirttim, V. (2006). The measurement of business intelligence. Information Systems Management, 23, 32–40. https://doi.org/10.1080/07366980903446611.

    Article  Google Scholar 

  30. Weske, M. (2012). Business process management. Berlin: Springer. https://doi.org/10.1007/978-3-642-28616-2.

  31. Object Management Group (OMG) (2011) Business process model and notation (BPMN) Version 2.0. Business, 50, 508. https://doi.org/10.1007/s11576-008-0096-z.

  32. Von Halle, B., & Goldberg, L. (2009). The decision model: A business logic framework linking business and technology. New York, NY: Taylor and Francis Group, LLC.

    Book  Google Scholar 

  33. Blenko, M.W., Mankins, M.C., Rogers, P.: The Decision-Driven Organization. Harv. Bus. Rev. 10 (2010).

    Google Scholar 

  34. Object Management Group. (2016). Decision model and notation.

    Google Scholar 

  35. Object Management Group. (2016). ArchiMate® 3.0 specification.

    Google Scholar 

  36. Shim, J., Merrill, W., Courtney, J., Power, D., Sharda, R., & Carlsson, C. (2002). Past, present and future of decision support system. Decision Support Systems, 33, 111–126.

    Article  Google Scholar 

  37. Chugh, R., & Grandhi, S. (2013). Why Business Intelligence? Significance of business intelligence tools and integrating BI governance with corporate governance. International Journal of Entrepreneurship and Innovation, 4, 1–14. https://doi.org/10.4018/ijeei.2013040101.

    Article  Google Scholar 

  38. Arnott, D., & Pervan, G. (2014). A critical analysis of decision support systems research revisited: The rise of design science. Journal of Information Technology, 29, 269–293. https://doi.org/10.1057/jit.2014.16.

    Article  Google Scholar 

  39. Taylor, Dillon, & Wingen, Van. (2010). Focus and diversity in information systems research: Meeting the dual demands of a healthy applied discipline. MIS Quarterly, 34, 647. https://doi.org/10.2307/25750699.

    Article  Google Scholar 

  40. van de Weerd, I., Brinkkemper, S. (2008). Meta-modeling for situational analysis and design methods. In: Handbook of research on modern systems analysis and design technologies and applications (vol. 35).

    Google Scholar 

  41. Han, J., Kamber, M., Pei, J. (2011). Data mining: Concepts and techniques. Morgan Kaufmann Publishers, Burlington, MA. https://doi.org/10.1016/B978-0-12-381479-1.00001-0.

  42. Witten, I. H., Frank, E., Hall, M. A., & Pal, C. (2016). Data mining: Practical machine learning tools and techniques. Burlington, MA: Morgan Kaufmann Publishers.

    Google Scholar 

  43. Kantardzic, M. (2011). Data mining : Concepts, models, methods, and algorithms. Wiley Online Library.

    Google Scholar 

  44. Rokach, L., Maimon, O. (2015). Data mining with decision trees: Theory and application.

    Google Scholar 

  45. Petrusel, R., Vanderfeesten, I., Dolean, C. C., Mican, D. (2011). Making decision process knowledge explicit using the decision data model. In: Business Information Systems, 340.

    Google Scholar 

  46. Berry, M. J. A., Linoff, G. S. (2004). Data mining techniques: for marketing, sales, and customer support. New York: Wiley.

    Google Scholar 

  47. Ross, Q. J. (1993).C4.5: Programs for machine learn. Morgan Kaufmann Publishers.

    Google Scholar 

  48. Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE transactions on information theory, 28, 129–137. https://doi.org/10.1109/TIT.1982.1056489.

    Article  Google Scholar 

  49. Vapnik, V. N. (1995). The nature of statistical learning theory. New York: Springer.

    Book  Google Scholar 

  50. Agrawal, R., Srikant, R. (1994). Fast algorithms for mining association rules in large databases. In Proceedings of the 20th International Conference on Very Large Data Bases (pp. 487–499). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.

    Google Scholar 

  51. Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees. Chapman & Hall/CRC. https://doi.org/10.1201/9781315139470.

    Article  Google Scholar 

  52. Wu, X., Kumar, V., Ross, Q. J., Ghosh, J., Yang, Q., Motoda, H., et al. (2008). Top 10 algorithms in data mining. In: Knowledge and information systems (pp. 1–37). https://doi.org/10.1007/s10115-007-0114-2.

  53. Dumas, M., La Rosa, M., Mendling, J., & Reijers, H. A. (2018). Fundamentals of business process management. Berlin: Springer.

    Book  Google Scholar 

  54. van der Aalst, W. M. P., Adriansyah, A., De Medeiros, A. K. A., Arcieri, F., Baier, T., Blickle, T., et al. (2012). Process mining manifesto. Lecture notes in business information processing. 99 LNBIP (pp. 169–194). https://doi.org/10.1007/978-3-642-28108-2_19.

  55. Rojas, E., Munoz-Gama, J., Sepúlveda, M., Capurro, D. (2016). Process mining in healthcare: A literature review. Journal of Biomedical Informatics, 61, 224–236. https://doi.org/10.1016/j.jbi.2016.04.007.

  56. Rozinat, A., & van der Aalst, W. M. P. (2008). Conformance checking of processes based on monitoring real behavior. Information Systems, 33, 64–95. https://doi.org/10.1016/j.is.2007.07.001.

    Article  Google Scholar 

  57. Rovani, M., Maggi, F. M., de Leoni, M., & van der Aalst, W. M. P. (2015). Declarative process mining in healthcare. Expert Systems with Applications, 42, 9236–9251. https://doi.org/10.1016/j.eswa.2015.07.040.

    Article  Google Scholar 

  58. Petri, C. A. (1966). Communication with Automata. Application Data Research, 15, 357–62. https://doi.org/AD0630125.

    Google Scholar 

  59. de Leoni, M., van der Aalst, W. M. P. (2013). Data-aware process mining: Discovering decisions in processes using alignments. In: Proceedings of the 28th Annual ACM Symposium on Applied Computing (pp. 1454–1461). dl.acm.org.

    Google Scholar 

  60. Kim, A., Obregon, J., Jung, J.-Y. (2014). Constructing decision trees from process logs for performer recommendation. In International Conference on Business Process Management (pp. 224–236) (2014). https://doi.org/10.1007/978-3-319-06257-0_18.

  61. Mannhardt, F., de Leoni, M., Reijers, H. A., van der Aalst, W. M. P.: Decision mining revisited-discovering overlapping rules. In International Conference on Advanced Information Systems Engineering (pp. 377–392).

    Google Scholar 

  62. de Leoni, M., Dumas, M., Garcķa-Bańuelos, L.: Discovering branching conditions from business process execution logs. In International Conference on Fundamental Approaches to Software Engineering (pp. 114–129). Berlin: Springer. https://doi.org/10.1007/978-3-642-37057-1.

  63. Petrusel, R. (2010). Decision mining and modeling in a virtual collaborative decision environment. Rijeka: In-Tech.

    Book  Google Scholar 

  64. Vanderfeesten, I., Reijers, H. A., van der Aalst, W. M. P. (2008). Product based workflow support: A recommendation service for dynamic workflow execution. In Proceedings of 20th International Conference on Advance Information System Engineering (pp. 571–574). https://doi.org/10.1007/978-3-540-69534-9_42.

  65. Sarno, R., Sari, P. L. I., Ginardi, H., Sunaryono, D., Mukhlash, I. (2013). Decision mining for multi choice workflow patterns. In Proceeding—2013 International Conference on Computer, Control, Informatics and Its Applications. Recent Challenges Computer, Control Informatics, IC3INA (pp. 337–342). https://doi.org/10.1109/IC3INA.2013.6819197.

  66. European Union. (2016). General data protection regulation. Official Journal of European Union, L119, 1–88 (2016).

    Google Scholar 

  67. Borg, M., Englund, C., Durán, B. (2017). Traceability and deep learning—safety-critical systems with traces ending in deep neural networks. In: Grand challenges of traceability: The next ten years (pp. 48–49). https://arxiv.org/abs/1710.03129.

  68. ISO. (2018). ISO 26262-2:2018.

    Google Scholar 

  69. Petri, C. A. (1962). Kommunikation mit Automaten.

    Google Scholar 

  70. De Smedt, J., Hasić, F., vanden Broucke, S. K. L. M., Vanthienen, J. (2017). Towards a holistic discovery of decisions in process-aware information systems. In International Conference on Business Processing Management (pp. 183–199). https://doi.org/10.1007/978-3-319-65000-5_11.

  71. Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., et al. (2017). Mastering the game of go without human knowledge. Nature, 550, 354.

    Article  Google Scholar 

  72. Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. Cambridge: MIT Press.

    Google Scholar 

  73. De Fauw, J., Ledsam, J. R., Romera-Paredes, B., Nikolov, S., Tomasev, N., Blackwell, S., et al. (2018). Clinically applicable deep learning for diagnosis and referral in retinal disease. Nature Medicine. https://doi.org/10.1038/s41591-018-0107-6.

    Article  Google Scholar 

  74. Lecun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521, 436–444. https://doi.org/10.1038/nature14539.

    Article  Google Scholar 

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Authors and Affiliations

  1. HU University of Applied Sciences Utrecht, Utrecht, The Netherlands

    Sam Leewis & Koen Smit

  2. Zuyd University of Applied Sciences, Sittard, The Netherlands

    Martijn Zoet

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  1. Sam Leewis
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  2. Koen Smit
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  3. Martijn Zoet
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Correspondence to Sam Leewis .

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Editors and Affiliations

  1. Department of Business and Economics, Parthenope University of Naples, Naples, Italy

    Rocco Agrifoglio

  2. Department of Business and Economics, Parthenope University of Naples, Naples, Italy

    Rita Lamboglia

  3. Faculty of Law, University of Teramo, Teramo, Italy

    Daniela Mancini

  4. Department of Management, University of Turin, Turin, Italy

    Francesca Ricciardi

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Leewis, S., Smit, K., Zoet, M. (2020). Putting Decision Mining into Context: A Literature Study. In: Agrifoglio, R., Lamboglia, R., Mancini, D., Ricciardi, F. (eds) Digital Business Transformation. Lecture Notes in Information Systems and Organisation, vol 38. Springer, Cham. https://doi.org/10.1007/978-3-030-47355-6_3

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