Abstract
Business Process Model and Notation (BPMN) is a well established standard for modeling and managing process knowledge of organizations. Recently, the Decision Model and Notation (DMN) standard has been proposed as a complementary technique to enact particular type of knowledge, namely the organizational rules (decision logic). An integrated model of processes and rules may bring numerous benefits to the knowledge management systems, but the modeling process itself is not a trivial task. To this end, methods that facilitate prototyping and semi-automatic construction of the integrated model are of great importance. In this paper, we propose a method for generating business processes with decisions in BPMN+DMN standards, using a prototyping method called ARD. We present an algorithm that, starting from an ARD model, generates an executable process model along with decision specification. Such a model can be treated as a structured rule base that provides explicit inference flow determined by the process control flow.
The paper is supported by the AGH UST research grant.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
d(f, t) denotes a dependency d from a property f to a property t.
- 3.
For simplicity, \(\mathcal {T}_{ Business Rule }\) will be denoted as \(\mathcal {T}_{ BR }\), and its elements as \(\tau ^1_{ BR }, \tau ^2_{ BR }\).
- 4.
The function derive(a, level) returns a set consisting of a conceptual attribute which was finalized into the given attribute a.
- 5.
If a particular conceptual attribute covers a single input attribute, create a User task “Enter name(a)” instead.
- 6.
The \(g_{+}\) parallel gateway is necessary if there are more than one BR tasks to be connected.
- 7.
This subset of output BR tasks should not be empty.
- 8.
If there is only one output attribute, its name should be used instead of name(c).
- 9.
For user-friendliness of task names, if the attribute t is of the symbolic type or derived one, the word “Determine” should be used in the task name. In other cases (i.e. numeric types), one can use the word “Calculate” instead.
- 10.
The conceptual attribute name can be found in the corresponding TPH model, if it is available for the algorithm. In other case, in the task name the names of all the attributes from the \(T_f\) set can be used.
References
Dumas, M., La Rosa, M., Mendling, J., Reijers, H.A.: Fundamentals of Business Process Management. Springer, Berlin (2013). https://doi.org/10.1007/978-3-642-33143-5
Forster, F.: The idea behind business process improvement: toward a business process improvement pattern framework, pp. 1–13. BPTrends, April 2006
Nalepa, G.J., Wojnicki, I.: Towards formalization of ARD+ conceptual design and refinement method. In Wilson, D.C., Lane, H.C. (eds.) FLAIRS-21: Proceedings of the Twenty-First International Florida Artificial Intelligence Research Society conference: 15–17 May 2008, Coconut Grove, Florida, USA, Menlo Park, California, pp. 353–358. AAAI Press (2008, accepted)
Nalepa, G.J., Ligęza, A.: Conceptual modelling and automated implementation of rule-based systems. In: Software Engineering: Evolution and Emerging Technologies. Volume 130 of Frontiers in Artificial Intelligence and Applications, pp. 330–340. IOS Press, Amsterdam (2005)
Atzmueller, M., Nalepa, G.J.: A textual subgroup mining approach for rapid ARD+ model capture. In Lane, H.C., Guesgen, H.W. (eds.) FLAIRS-22: Proceedings of the Twenty-Second International Florida Artificial Intelligence Research Society Conference: 19–21 May 2009, Sanibel Island, Florida, USA, Menlo Park, California, FLAIRS, pp. 414–415. AAAI Press (2009, to be published)
Kluza, K., Nalepa, G.J.: A method for generation and design of business processes with business rules. Inform. Softw. Technol. 91, 123–141 (2017)
Bazhenova, E., Weske, M.: Deriving decision models from process models by enhanced decision mining. In: Reichert, M., Reijers, H.A. (eds.) BPM 2015. LNBIP, vol. 256, pp. 444–457. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-42887-1_36
Batoulis, K., Meyer, A., Bazhenova, E., Decker, G., Weske, M.: Extracting decision logic from process models. In: Zdravkovic, J., Kirikova, M., Johannesson, P. (eds.) CAiSE 2015. LNCS, vol. 9097, pp. 349–366. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19069-3_22
Vanthienen, J., Wets, G.: From decision tables to expert system shells. Data Knowl. Eng. 13(3), 265–282 (1994)
Kluza, K., Nalepa, G.J.: Towards rule-oriented business process model generation. In Ganzha, M., Maciaszek, L.A., Paprzycki, M. (eds.) Proceedings of the Federated Conference on Computer Science and Information Systems - FedCSIS 2013, Krakow, Poland, 8–11 September 2013, pp. 959–966. IEEE (2013)
Bazhenova, E., Zerbato, F., Oliboni, B., Weske, M.: From BPMN process models to dmn decision models. Inform. Syst. 83, 69–88 (2019)
De Smedt, J., Hasić, F., vanden Broucke, S.K.L.M., Vanthienen, J.: Towards a holistic discovery of decisions in process-aware information systems. In: Carmona, J., Engels, G., Kumar, A. (eds.) BPM 2017. LNCS, vol. 10445, pp. 183–199. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-65000-5_11
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Kluza, K., Wiśniewski, P., Adrian, W.T., Ligęza, A. (2019). From Attribute Relationship Diagrams to Process (BPMN) and Decision (DMN) Models. In: Douligeris, C., Karagiannis, D., Apostolou, D. (eds) Knowledge Science, Engineering and Management. KSEM 2019. Lecture Notes in Computer Science(), vol 11775. Springer, Cham. https://doi.org/10.1007/978-3-030-29551-6_55
Download citation
DOI: https://doi.org/10.1007/978-3-030-29551-6_55
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-29550-9
Online ISBN: 978-3-030-29551-6
eBook Packages: Computer ScienceComputer Science (R0)