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A Framework for Optimising Business Rules

  • Alan DormerEmail author
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 303)

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%.

Keywords

Business intelligence Business rules Analytics Optimisation Decision support Services Productivity 

References

  1. 1.
  2. 2.
    Negash, S.: Business intelligence. Commun. Assoc. Inf. Syst. 13 (2004). Article 15. http://aisel.aisnet.org/cais/vol13/iss1/15
  3. 3.
    Andreescu, A.: Methodological approaches based on business rules. Inform. Econ. J. 12(3), 23–27 (2008)Google Scholar
  4. 4.
    Taylor, J.: Decision Management Systems: A Practical Guide to Using Business Rules and Predictive Analytics. IBM Press, Indianapolis (2011)Google Scholar
  5. 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 CrossRefGoogle Scholar
  6. 6.
    Vergidis, K.: Business process optimisation using and evolutionary multi-objective framework. Ph.D. thesis (2008)Google Scholar
  7. 7.
    Dormer, A.: Hybrid Optimisation System for Solving Planning and Scheduling Problems. COR/INFORMS, Banff (2004)Google Scholar
  8. 8.
    Ernst, A., et al.: Staff scheduling and rostering: a review of applications, methods and models. Eur. J. Oper. Res. 153, 3–27 (2004)CrossRefGoogle Scholar
  9. 9.
    Zachary, H., et al.: Supply-chain optimisation – players, tools and issues. OR Insight 14, 20–30 (2001)CrossRefGoogle Scholar
  10. 10.
    Drucker, P.F.: The new productivity challenge. Harv. Bus. Rev. 69(6), 69 (1991)Google Scholar
  11. 11.
    Teodoru, S.F.: Business process management integration solution in financial sector. Inform. Econ. 13(1), 47 (2009)Google Scholar
  12. 12.
    Vergidis, K.: An evolutionary multi-objective framework for business process optimisation. Appl. Soft Comput. 12(2), 2638–2653 (2012)CrossRefGoogle Scholar
  13. 13.
    The Business Rules Group. Final Report, Revision 1.3, July 2000Google Scholar
  14. 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. 13/177,998Google Scholar
  15. 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. 13/594,963Google Scholar
  16. 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. 17.
    Gottesdeiner, E.: Capturing business rules. Softw. Dev.-San Franc. 7, 72 (1999)Google Scholar
  18. 18.
    Shao, J., Pound, C.J.: Extracting business rules from information systems. BT Technol. J. 17(4), 179–186 (1999)CrossRefGoogle Scholar
  19. 19.
    Chikofsky, E.J., Cross, J.H.: Reverse engineering and design recovery: a taxonomy. IEEE Softw. 7(1), 13–17 (1990)CrossRefGoogle Scholar
  20. 20.
    Chisholm, M.: How to Build a Business Rules Engine: Extending Application Functionality through Metadata Engineering. Morgan Kaufmann, Burlington (2004)Google Scholar
  21. 21.
    Kardasis, P., Loucopoulos, P.: Expressing and organising business rules. Inf. Softw. Technol. 46(11), 701–718 (2004)CrossRefGoogle Scholar
  22. 22.
    Rosca, D., Wild, C.: Towards a flexible deployment of business rules. Expert Syst. Appl. 23(4), 385–394 (2002)CrossRefGoogle Scholar
  23. 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. 24.
    Gottesdiener, E.: Business rules show power, promise. Appl. Dev. Trends 4(3), 36–42 (1997)Google Scholar
  25. 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. 26.
    Graml, T., Bracht, R., Spies, M.: Patterns of business rules to enable agile business processes. Enterp. Inf. Syst. 2(4), 385–402 (2008)CrossRefGoogle Scholar
  27. 27.
    Appleton, D.S.: Business rules - the missing link. Datamation 30(17), 145 (1984)Google Scholar
  28. 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. 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. 30.
    Jandir, R.: Event based propagation approach to constraint configuration problems. Master’s theses, 3659 (2009). http://scholarworks.sjsu.edu/etd_theses/3659
  31. 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. 32.
    Boyer, J., Mili, H.: Agile Business Rule Development’ Process, Architecture, and JRules Examples. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  33. 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. 34.
  35. 35.
  36. 36.
  37. 37.
  38. 38.
  39. 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. 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. 41.
    Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Wadsworth International, Belmont (1984)Google Scholar
  42. 42.
    Bundy, A., Siver, B., Plummer, D.: An analytical comparison of some rule learning programs. Artif. Intell. 27, 137–181 (1985)CrossRefGoogle Scholar
  43. 43.

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Information TechnologyMonash UniversityClaytonAustralia

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