Enhancing Decision-Making Consistency in Business Process using a Rule-Based Approach Case of Business Intelligence Process

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Riadh Ghlala
Zahra Kodia
Lamjed Ben Said


Decision-making in Business process, Consistency, RETE Algorithm, MongoDB, Business intelligence


Decision-making in Business Process is a real challenge, given its technical complexity and organizational impact. Mostly, decision-making is based on business rules fired by an inference engine using facts reflecting the context of the current process task. Focus on a task alone and in isolation from the rest of the process can easily lead to inconsistency in decision-making. In this paper, we aim to improve the importance of consistency of decision-making throughout the process.

To fulfill this aim, our contribution is to propose Consistency Working Memory RETE (CWM-RETE): a Framework based on the Rete Algorithm as a pattern-matching algorithm to simulate inference; and MongoDB as a document-oriented database to serialize business rules. This framework enables the compatibility of decision-making throughout the business process. The experimentation is based on the Business Intelligence process as a case study and it is shown that the decision-making process can generate different results depending on whether consistency functionality is enabled or not.


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Alnoukari, M. (2022). From business intelligence to big data: The power of analytics. (I. Global, Ed.) Research Anthology on Big Data Analytics, Architectures, and Applications, 823–841.
Argandona, A. (2008). Consistency in decision making in companies. In I. B. School (Ed.), Humanizing the Firm and the Management Profession. Barcelona.
Awadid, A., & Nurcan, S. (2016). Towards enhancing business process modeling formalisms of EKD with consistency consideration. IEEE Tenth International Conference on Research Challenges in Information Science (RCIS), 1-12.
Awadid, A., & Nurcan, S. (2019). Consistency requirements in business process modeling: a thorough overview. Software & Systems Modeling, 18(2), 1097–1115.
Baiyere, A., Salmela, H., & Tapanainen, T. (2020). Digital transformation and the new logics of business process management. European Journal of Information Systems, 29(3), 238–259.
Bajwa, I. S., Lee, M. G., & Bordbar, B. (2011). SBVR Business Rules Generation from Natural Language Specification. Artificial Intelligence for Business Agility AAAI Spring Symposium Series, 2–8.
Batoulis, K., Meyer, A., Bazhenova, E., Decker, G., & Weske, W. (2015). Extracting Decision Logic from Process Models. Proceedings of the 27th International Conference on Advanced Information Systems Engineering CAiSE, 349–366. Stockholm.
Biard, T., LeMauff, A., Bigand, M., & Bourey, J. (2015). Separation of Decision Modeling from Business Process Modeling using new Decision Model and Notation (DMN) for automating operational Decision-making. Proceedings 16th IFIP Working Conference on Virtual Enterprises Risks and Resilience of Collaborative Networks, PRO-VE, 489–496. Albi, France.
Branco, M. (2014). Managing Consistency of Business Process Models across Abstraction Levels. ON, Canada: University of Waterloo, Waterloo. Retrieved from http://hdl.handle.net/10012/8310
Branco, M., Xiong, Y., Czarnecki, K., Küster, J., & Völzer, H. (2014). A case study on consistency management of business and IT process models in banking. Software and Systems Modeling (SoSyM), 13(3), 913–940. https://doi.org/10.1007/s10270-013-0318-8
Cheng, C. Z., & Cao, L. (2020). Facilitating speed of internationalization: The roles of business intelligence and organizational agility. Journal of Business Research, 110, 95–103.
Dokhanchi, A., & Nazemi, E. (2015). BISC: A Framework for Aligning Business Intelligence with Corporate Strategies Based on Enterprise Architecture Framework. International Journal of Enterprise Information Systems, 11(2), 90–106.
Finch, E., Geddes, E., & Larin, H. (2005). Ethically-based clinical decision-making in physical therapy: process and issues. Physiotherapy Theory and Practice, 21(3), 147–162. https://doi.org/10.1080/09593980590922271
Finlay, B., & Ogden, R. (2012). Consistency in Tribunal Decision-Making. Canadian Journal of Administrative Law and Practice, 25, 277–288.
Forgy, C. (1982). Rete: A Fast Algorithm for the Many Pattern/Many Object Pattern Match Problem. Artificial Intelligence, 19, 17–37.
Gartner. (2017). IT Glossary. Retrieved from https://www.gartner.com/it-glossary/business-process
Gartner. (2017). Reviews for Business Process Management Platforms. Retrieved from https://www.gartner.com/reviews/market/business-process-management-platforms
Ghlala, R., Kodia Aouina, Z., & BenSaid, L. (2016). BPMN Decision Footprint: Towards Decision Harmony Along BI Process. In D. R. Dregvaite G. (Ed.), International Conference on Information and Software Technologies, 269–284. Springer, Cham.
Ghlala, R., Kodia Aouina, Z., & BenSaid, L. (2016). Decision-making harmonization in business process: Using NoSQL databases for decision rules modelling and serialization. 4th International Conference on Control Engineering and Information Technology (CEIT).
Goedertier, S., & Vanthienen, J. (2005). Rule-based business process modeling and execution. CTIT. Proceedings of the IEEE EDOC Workshop on Vocabularies Ontologies and Rules for The Enterprise. Enschede: Workshop Proceeding Series.
Humm, B., & Fengel, J. (2012). Assessing semantic consistency of business process models. International Conference on Business Information Systems (ICBIS), 321–326. Paris, France.
Kluza, K., & Nalepa, G. J. (2013). Towards Rule-oriented Business Process Model Generation. Proceedings of the Federated Conference on Computer Science and Information Systems, FedCSIS, 939–946. Kraków, Poland.
Lea, B. R., Yu, W. B., & Min, H. (2018). Data visualization for assessing the biofuel commercialization potential within the business intelligence framework. Journal of Cleaner Production, 188, 921–941.
Mafazi, S. G. (2015). Consistent abstraction of business processes based on constraints. Journal on Data Semantics, 59–78.
Mei, H., Guan, H., Xin, C., Wen, X., & Chen, W. (2020). Datav: Data visualization on large high-resolution displays. Visual Informatics, 4(3), 12–23.
Mitrovic, S. (2020). Adapting of international practices of using business-intelligence to the economic analysis in Russia. Cham: Springer.
Mojzisch, A., Kerschreiter, R., Faulmüller, N., Vogelgesang, F., & Schulz-Hardt, S. (2014). The consistency principle in interpersonal communication: Consequences of preference confirmation and disconfirmation in collective decision making. Journal of Personality and Social Psychology, 106(6), 961–977.
Negash, S., & Gray, P. (2008). Business Intelligence. (SpringerLink, Ed.) Handbook on Decision Support Systems 2, Part of the series International Handbooks Information System, 175–193.
OMG. (2014). Business Process Modeling Notation Specification 2.0.2. Retrieved from http://www.omg.org/spec/BPMN/2.0.2/PDF/
OMG. (2016). Decision Model and Notation 1.1. Retrieved from http://www.omg.org/spec/DMN/1.1/PDF
Piotr, W., Krzysztof, K., & Antoni, L. (2018). An Approach to Participatory Business Process Modeling: BPMN Model Generation Using Constraint Programming and Graph Composition. Applied Sciences, 1428.
Romero, H., Dijkman, R., Grefen, P., & Van Weele, A. (2012). Harmonization of Business Process Models. In F. B. Daniel (Ed.), BPM 2011 Workshops, Part I. LNBIP, 99, 13–24. Heidelberg: Springer.
Ross, J. W., Weill, P., & Robertson, D. (2006). Enterprise architecture as strategy: Creating a foundation for business execution. Harvard Business Press.
Runte, W. (2012). Enhancing Business Process Management with a Constraint Approach. New Trends in Software Methodologies, Tools and Techniques, 215–237.
Saggi, M. K., & Jain, S. (2018). A survey towards an integration of big data analytics to big insights for value-creation. Information Processing & Management, 54(5), 758–790.
Schweitzer, M. E., & Gibson, D. E. (2008). Fairness, feelings, and ethical decision making: Consequences of violating community standards of fairness. Journal of Business Ethics, 77(3), 287–301.
Shariff, A., Sridhar, S. B., Basha, N. A., Alshemeil, S. S., & Alzaabi, N. A. (2022). Development and validation of standardized severity rating scale to assess the consistency of drug-drug interaction severity among various drug information resources. Research in Social and Administrative Pharmacy. https://doi.org/10.1016/j.sapharm.2021.12.006
Skersys, T., Tutkute, L., & Butleris, R. (2012). The Enrichment of BPMN Business Process Model with SBVR Business Vocabulary and Rules. Journal of Computing and Information Technology, 20(3), 143–150.
Taylor, J., Fish, A., & Vincent, P. (2013). Emerging Standards in Decision Modeling - an Introduction to Decision Model and Notation in iBPMS Intelligent BPM Systems: Impact and Opportunity. Retrieved from Future Strategies Inc.
Torre, D., Labiche, Y., Genero, M., & Elaasar, M. (2018). A systematic identification of consistency rules for UML diagrams. Journal of Systems and Software, 144, 121–142.
Van De Water, S., Renaux, T., Lode, H., & Wolfgang, D. (2015). Indexing RETE's Working Memory: Catering to Dynamic Changes of the Ruleset Pittsburgh. REBLS 2015: Reactive and Event-based Languages and Systems.
Wang, J., Zhou, R., Li, J., & Wang, G. (2014). A distributed rule engine based on message-passing model to deal with big data. Lecture Notes on Software Engineering, 2(3), 275–281.
Winter, R., & Fischer, R. (2006). Essential Layers, Artifacts, and Dependencies of Enterprise Architecture. 10th IEEE International Enterprise Distributed Object Computing Conference Workshops (EDOCW'06), 30–35.
Wüllenweber, K., Koenig, W., Beimborn, D., & Weitzel, T. (2009). The impact of process standardization on business process outsourcing success. (Springer, Ed.) Information Systems Outsourcing, 527–548. https://doi.org/10.1007/s10796-008-9063-x
Yay, E., Martínez Madrid, N., & Ortega Ramírez, J. (2014). Using an improved rule match algorithm in an expert system to detect broken driving rules for an energy-efficiency and safety relevant driving system. Procedia Computer Science, 35, 127–136.