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A Framework for Optimising Business Rules

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Business Information Systems Workshops (BIS 2017)

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Abstract

There has been significant growth in the number of business intelligence platforms that support and execute business rules since the late 1990s that shows no signs of abating. This paper examines the question of how to optimize business rules that can support rather than replace the human decision maker. It presents a novel framework to combine data (including decisions and actual outcomes), a business rules engine and the human judge. Preliminary results, on real data, suggest that about 80% of cases could be determined by a rules engine with an overall increase in gross profit of about 2%.

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References

  1. Gartner Group. http://www.gartner.com/it-glossary/business-intelligence-bi/

  2. Negash, S.: Business intelligence. Commun. Assoc. Inf. Syst. 13 (2004). Article 15. http://aisel.aisnet.org/cais/vol13/iss1/15

  3. Andreescu, A.: Methodological approaches based on business rules. Inform. Econ. J. 12(3), 23–27 (2008)

    Google Scholar 

  4. Taylor, J.: Decision Management Systems: A Practical Guide to Using Business Rules and Predictive Analytics. IBM Press, Indianapolis (2011)

    Google Scholar 

  5. Harmon, P.: Business process management: today and tomorrow. In: Dumas, M., Reichert, M., Shan, M.-C. (eds.) BPM 2008. LNCS, vol. 5240, p. 1. Springer, Heidelberg (2008). doi:10.1007/978-3-540-85758-7_1

    Chapter  Google Scholar 

  6. Vergidis, K.: Business process optimisation using and evolutionary multi-objective framework. Ph.D. thesis (2008)

    Google Scholar 

  7. Dormer, A.: Hybrid Optimisation System for Solving Planning and Scheduling Problems. COR/INFORMS, Banff (2004)

    Google Scholar 

  8. Ernst, A., et al.: Staff scheduling and rostering: a review of applications, methods and models. Eur. J. Oper. Res. 153, 3–27 (2004)

    Article  Google Scholar 

  9. Zachary, H., et al.: Supply-chain optimisation – players, tools and issues. OR Insight 14, 20–30 (2001)

    Article  Google Scholar 

  10. Drucker, P.F.: The new productivity challenge. Harv. Bus. Rev. 69(6), 69 (1991)

    Google Scholar 

  11. Teodoru, S.F.: Business process management integration solution in financial sector. Inform. Econ. 13(1), 47 (2009)

    Google Scholar 

  12. Vergidis, K.: An evolutionary multi-objective framework for business process optimisation. Appl. Soft Comput. 12(2), 2638–2653 (2012)

    Article  Google Scholar 

  13. The Business Rules Group. Final Report, Revision 1.3, July 2000

    Google Scholar 

  14. Gupta, A.K., Lotlikar, R.M., Angshu, R.: System and Method for Determining Interpersonal Relationship Influence Information using Textual Content from Interpersonal Interactions. U.S. Patent Application No. 13/177,998

    Google Scholar 

  15. Gupta, A.K., Lotlikar, R.M., Angshu, R.: Method for Determining Interpersonal Relationship Influence Information using Textual Content from Interpersonal Interactions. U.S. Patent Application No. 13/594,963

    Google Scholar 

  16. Sneed, H.M., Erdos, K.: Extracting business rules from source code. In: Proceedings of Fourth Workshop on Program Comprehension. IEEE (1996)

    Google Scholar 

  17. Gottesdeiner, E.: Capturing business rules. Softw. Dev.-San Franc. 7, 72 (1999)

    Google Scholar 

  18. Shao, J., Pound, C.J.: Extracting business rules from information systems. BT Technol. J. 17(4), 179–186 (1999)

    Article  Google Scholar 

  19. Chikofsky, E.J., Cross, J.H.: Reverse engineering and design recovery: a taxonomy. IEEE Softw. 7(1), 13–17 (1990)

    Article  Google Scholar 

  20. Chisholm, M.: How to Build a Business Rules Engine: Extending Application Functionality through Metadata Engineering. Morgan Kaufmann, Burlington (2004)

    Google Scholar 

  21. Kardasis, P., Loucopoulos, P.: Expressing and organising business rules. Inf. Softw. Technol. 46(11), 701–718 (2004)

    Article  Google Scholar 

  22. Rosca, D., Wild, C.: Towards a flexible deployment of business rules. Expert Syst. Appl. 23(4), 385–394 (2002)

    Article  Google Scholar 

  23. Cibrán, M., D’hondt, M., Jonckers, V.: Aspect-oriented programming for connecting business rules. In: Proceedings of the 6th International Conference on Business Information Systems, vol. 6, no. 7 (2003)

    Google Scholar 

  24. Gottesdiener, E.: Business rules show power, promise. Appl. Dev. Trends 4(3), 36–42 (1997)

    Google Scholar 

  25. Van Eijndhoven, T., Iacob, M., Ponisio, M.L.: Achieving business process flexibility with business rules. In: 12th International Conference on Enterprise Distributed Object Computing. IEEE (2008)

    Google Scholar 

  26. Graml, T., Bracht, R., Spies, M.: Patterns of business rules to enable agile business processes. Enterp. Inf. Syst. 2(4), 385–402 (2008)

    Article  Google Scholar 

  27. Appleton, D.S.: Business rules - the missing link. Datamation 30(17), 145 (1984)

    Google Scholar 

  28. Leite, J.C.S., Leonardi, M.C.: Business rules as organizational policies. In: Proceedings of the 9th International Workshop on Software Specification and Design. IEEE Computer Society (1998)

    Google Scholar 

  29. Liu, F., et al.: Risk Assessment Rule Set Application for Fraud Prevention. U.S. Patent No. 8,924,279. 30 (2014)

    Google Scholar 

  30. Jandir, R.: Event based propagation approach to constraint configuration problems. Master’s theses, 3659 (2009). http://scholarworks.sjsu.edu/etd_theses/3659

  31. Begunov, N., Moskalev, I., Klebanov, B.: City agent-based model. In: Proceedings of the 2008 Spring Simulation Multiconference. Society for Computer Simulation International (2008)

    Google Scholar 

  32. Boyer, J., Mili, H.: Agile Business Rule Development’ Process, Architecture, and JRules Examples. Springer, Heidelberg (2011)

    Book  Google Scholar 

  33. Dormer, A.: Optimising business rules in the services sector. Int. J. Soc. Behav. Educ. Econ. Bus. Ind. Eng. 6(10), 2580–2584 (2012)

    Google Scholar 

  34. Graydon. https://www.graydon.co.uk/downloads/epaper-new-era-customer-acceptance-decision-model. Accessed 13 Dec 2016

  35. RMS. http://www.rms.nsw.gov.au/documents/business-industry/examiners/business-rules-authorised-inspection-station-scheme.pdf. Accessed 13 Dec 2016

  36. NHFP. http://www.publichospitalfunding.gov.au/Media/Business%20Rules%20Volume%202.pdf. Accessed 13 Dec 2016

  37. Gartner. https://www.gartner.com/doc/1926217/vendors-business-rule-market. Accessed 13 Dec 2016

  38. Salescycle. https://blog.salecycle.com/strategies/form-abandonment-can-avoid. Accessed 13 Dec 2016

  39. Hall, M.: Correlation-based feature selection of discrete and numeric class machine learning. In: Proceedings ICML 2000 Seventh International Conference on Machine Learning, 29 June–02 July, pp. 359–366 (2000)

    Google Scholar 

  40. Brunswik, E.: The Essential Brunswik: Beginnings, Explications, Applications, New Directions in Research on Decision Making, Research Conference on Subjective Probability, Utility and Decision Making (1985)

    Google Scholar 

  41. Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Wadsworth International, Belmont (1984)

    Google Scholar 

  42. Bundy, A., Siver, B., Plummer, D.: An analytical comparison of some rule learning programs. Artif. Intell. 27, 137–181 (1985)

    Article  Google Scholar 

  43. https://www.lendingclub.com/info/download-data.action

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Dormer, A. (2017). A Framework for Optimising Business Rules. In: Abramowicz, W. (eds) Business Information Systems Workshops. BIS 2017. Lecture Notes in Business Information Processing, vol 303. Springer, Cham. https://doi.org/10.1007/978-3-319-69023-0_1

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