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Decision model change patterns for dynamic system evolution

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Abstract

In the modern digital era, information systems must operate in increasingly interconnected and dynamic environments, which force them to be changeable yet consistent. Such modern information systems are usually decision- and knowledge-intensive. A recently introduced standard, the decision model and notation (DMN), has been adopted in both industry and academia as a suitable method for modelling decisions and decision rules. Noteworthy is that, despite the dynamic nature of modern knowledge-intensive systems, DMN was only studied and implemented in a static fashion, as decision schema change patterns have not received any attention so far. This paper identifies and analyses the change patterns that can occur in a DMN decision model. A change in the decision model can require the triggering of other changes in order to safeguard consistency. As such, this paper will also investigate for each change pattern which further changes should be performed to ensure model consistency. The patterns presented in this paper will not only facilitate the understanding of decision change management and within-model consistency, but can also be capitalised on for developing and implementing flexible decision management systems. To illustrate this, we present a modelling environment prototype that provides modelling support when applying the proposed change patterns.

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Notes

  1. https://camunda.com/download/modeler/.

References

  1. OMG (2019) Decision Model and Notation (DMN) 1.2

  2. Hasić F, De Smedt J, Vanthienen J (2018) Augmenting processes with decision intelligence: principles for integrated modelling. Decis Support Syst 107:1–12

    Article  Google Scholar 

  3. Bork D, Karagiannis D, Pittl B (2018) Systematic analysis and evaluation of visual conceptual modeling language notations. In: 2018 12th international conference on research challenges in information science (RCIS). IEEE, pp 1–11

  4. Hasić F, Vanthienen J (2019) Complexity metrics for DMN decision models. Comput Stand Interfaces 65:15–37. https://doi.org/10.1016/j.csi.2019.01.006

    Article  Google Scholar 

  5. Hinkelmann K, Pierfranceschi A, Laurenzi E (2016) The knowledge work designer-modelling process logic and business logic. In: Modellierung (workshops), pp 135–140

  6. Deryck M, Hasić F, Vanthienen J, Vennekens J (2018) A case-based inquiry into the decision model and notation (DMN) and the knowledge base (KB) paradigm. In: Benzmüller C, Ricca F, Parent X, Roman D (eds) Rules reasoning. Springer, Cham, pp 248–263

    Chapter  Google Scholar 

  7. Bork D, Buchmann R, Karagiannis D, Lee M, Miron E-T (2019) An open platform for modeling method conceptualization: the omilab digital ecosystem. Commun Assoc Inf Syst 44:673–697. https://doi.org/10.17705/1CAIS.04432

    Article  Google Scholar 

  8. Hasić F, Vanthienen J (2019) From decision knowledge to e-government expert systems: the case of income taxation for foreign artists in Belgium. Knowl Inf Syst. https://doi.org/10.1007/s10115-019-01416-4

    Article  Google Scholar 

  9. Campos J, Richetti P, Baião FA, Santoro FM (2017) Discovering business rules in knowledge-intensive processes through decision mining: an experimental study. In: International conference on business process management. Springer, pp 556–567

  10. Santoro FM, Baião FA (2017) Knowledge-intensive process: a research framework. In: International conference on business process management. Springer, pp 460–468

  11. Corea C, Delfmann P (2018) A tool to monitor consistent decision-making in business process execution. Proceedings of the Dissertation Award, Demonstration, and Industrial Track at BPM 9–14

  12. Hinkelmann K (2016) Business process flexibility and decision-aware modeling-the knowledge work designer. In: Domain-specific conceptual modeling. Springer, pp 397–414

  13. Nagel S, Corea C, Delfmann P (2019) Effects of quantitative measures on understanding inconsistencies in business rules. In: Proceedings of the 52nd Hawaii international conference on system sciences

  14. Figl K, Mendling J, Tokdemir G, Vanthienen J (2018) What we know and what we do not know about dmn. Enterp Model Inf Syst Archit 13:1–2

    Google Scholar 

  15. Hasić F, De Smedt J, Vanthienen J (2017) An illustration of five principles for integrated process and decision modelling (5PDM). SSRN 3082752

  16. Hu J, Aghakhani G, Hasić F, Serral E (2017) An evaluation framework for design-time context-adaptation of process modelling languages. In: IFIP working conference on the practice of enterprise modeling. Springer, pp 112–125

  17. Hasić F, De Smedt J, Vanden Broucke S, Serral Asensio E (2020) Decision as a service (DAAS): a service-oriented architecture approach for decisions in processes. IEEE Trans Serv Comput. https://doi.org/10.1109/TSC.2020.2965516

    Article  Google Scholar 

  18. Hasić F, Serral Asensio E (2019) Change patterns for decision model and notation (DMN) model evolution. In: Proceedings of the 18th Belgium-Netherlands software evolution workshop (BENEVOL), CEUR-WS.org

  19. Hallerbach A, Bauer T, Reichert M (2010) Configuration and management of process variants. In: Handbook on business process management, vol 1. Springer, pp 237–255

  20. Hasić F, De Smedt J, Vanthienen J (2018) Developing a modelling and mining framework for integrated processes and decisions. In: Debruyne C, Panetto H, Weichhart G, Bollen P, Ciuciu I, Vidal M-E, Meersman R (eds) On the move to meaningful internet systems. OTM 2017 workshops. Springer, Cham, pp 259–269

    Chapter  Google Scholar 

  21. Hasić F, De Smedt J, Vanthienen J (2018) Redesigning processes for decision-awareness: strategies for integrated modelling. In: International conference on the quality of information and communications technology

  22. Hasić F, Vanwijck L, Vanthienen J (2017) Integrating processes, cases, and decisions for knowledge-intensive process modelling. In: International workshop on practicing open enterprise modeling, CEUR

  23. Biard T, Bourey J-P, Bigand M, Bocquet J-C (2017) Modélisation des prises de décisions dans les processus métier grâce à DMN (decision model and notation). In: 12ème Congrès International de Génie Industriel 2017

  24. Biard T, Bourey J-P, Bigand M (2017) DMN (decision model and notation): De la modélisation à l’automatisation des décisions. In: INFORSID 2017

  25. Biard T, Le Mauff A, Bigand M, Bourey J-P (2015) Separation of decision modeling from business process modeling using new “decision model and notation” (DMN) for automating operational decision-making. In: Working conference on virtual enterprises. Springer, pp 489–496

  26. Batoulis K, Baumgraß A, Herzberg N, Weske M (2015) Enabling dynamic decision making in business processes with DMN. In: International conference on business process management. Springer, pp 418–431

  27. De Smedt J, Hasić F, van den Broucke S, Vanthienen J (2019) Holistic discovery of decision models from process execution data. Knowl Based Syst 183:104866. https://doi.org/10.1016/j.knosys.2019.104866

    Article  Google Scholar 

  28. Batoulis K, Weske M. Soundness of decision-aware business processes. Working paper. goo.gl/nhtCZ7

  29. Batoulis K, Haarmann S, Weske M (2017) Various notions of soundness for decision-aware business processes. In: International conference on conceptual modeling. Springer, pp 403–418

  30. Hinkelmann K, Kritikos K, Kurjakovic S, Lammel B, Woitsch R (2016) A modelling environment for business process as a service. In: International conference on advanced information systems engineering. Springer, pp 181–192

  31. von Halle B, Goldberg L (2019) The decision model: a business logic framework linking business and technology. Taylor and Francis, LLC, London

    Google Scholar 

  32. von Halle B, Goldberg L (2010) The decision model# 2: Improving process models and the requirements process. Knowledge Partners International, LLC, London

    Google Scholar 

  33. dos Santos França J B, Netto J M, Carvalho J do ES, Santoro F M, Baião F A, Pimentel M (2015) Kipo: the knowledge-intensive process ontology. Softw Syst Model 14(3):1127–1157

    Article  Google Scholar 

  34. Santoro F, Slaats T, Hildebrandt TT, Baiao F (2019) Dcr-kipn a hybrid modeling approach for knowledge-intensive processes. In: International conference on conceptual modeling. Springer, pp 153–161

  35. Bellahsene Z (2002) Schema evolution in data warehouses. Knowl Inf Syst 4(3):283–304

    Article  Google Scholar 

  36. Noy NF, Klein M (2004) Ontology evolution: not the same as schema evolution. Knowl Inf Syst 6(4):428–440

    Article  Google Scholar 

  37. Kadir WMW, Loucopoulos P (2005) Linking and propagating business rule changes to is design. In: Information systems development. Springer, pp 253–264

  38. Boyer J, Mili H (2011) Agile business rule development. In: Agile business rule development. Springer, pp 49–71

  39. Corradini F, Polzonetti A, Riganelli O (2018) Business rules in e-government applications, arXiv preprint arXiv:1802.08484

  40. Szvetits M, Zdun U (2016) Systematic literature review of the objectives, techniques, kinds, and architectures of models at runtime. Softw Syst Mode 15(1):31–69

    Article  Google Scholar 

  41. Blair G, Bencomo N, France RB (2009) Models@ run. time. Computer 42(10):22–27

    Article  Google Scholar 

  42. Loukil S, Kallel S, Jmaiel M (2017) An approach based on runtime models for developing dynamically adaptive systems. Future Gen Comput Syst 68:365–375

    Article  Google Scholar 

  43. Wombacher A, Rozie M (2006) Evaluation of workflow similarity measures in service discovery. Serv Oriented Electron Commer 80:51–71

    Google Scholar 

  44. Calero C, Piattini M, Genero M (2001) Empirical validation of referential integrity metrics. Inf Softw Technol 43(15):949–957. https://doi.org/10.1016/S0950-5849(01)00202-6

    Article  Google Scholar 

  45. Ordonez C, Garca-Garca J (2008) Referential integrity quality metrics. Decis Support Syst 44(2):495–508. https://doi.org/10.1016/j.dss.2007.06.004

    Article  Google Scholar 

  46. Smit K, Zoet M, Berkhout M (2017) Verification capabilities for business rules management in the Dutch governmental context. In: 2017 international conference on research and innovation in information systems (ICRIIS). IEEE, pp 1–6

  47. Batoulis K, Nesterenko A, Repitsch G, Weske M (2017) Decision management in the insurance industry: standards and tools. In: BPM (Industry Track), pp 52–63

  48. Bazhenova E, Zerbato F, Oliboni B, Weske M (2019) From bpmn process models to dmn decision models. Inf Syst 83:69–88

    Article  Google Scholar 

  49. OMG (2011) Business process model and notation (BPMN) 2.0

  50. Hassanalieragh M, Page A, Soyata T, Sharma G, Aktas M, Mateos G, Kantarci B, Andreescu S (2015) Health monitoring and management using Internet-of-Things (IoT) sensing with cloud-based processing: opportunities and challenges. In: 2015 IEEE international conference on services computing (SCC), IEEE, pp 285–292

  51. Horita FEA, Link D, de Albuquerque JP, Hellingrath B (2016) ODMN: an integrated model to connect decision-making needs to emerging data sources in disaster management. In: 2016 49th Hawaii international conference on system sciences (HICSS). IEEE, pp 2882–2891

  52. Horita FE, de Albuquerque JP, Marchezini V, Mendiondo EM (2017) Bridging the gap between decision-making and emerging big data sources: an application of a model-based framework to disaster management in brazil. Decis Support Syst 97:12–22

    Article  Google Scholar 

  53. Calvanese D, Dumas M, Laurson Ü, Maggi FM, Montali M, Teinemaa I (2018) Semantics, analysis and simplification of dmn decision tables. Inf Syst 78:112–125

    Article  Google Scholar 

  54. Wets G, Vanthienen J, Timmermans H (1998) Modelling decision tables from data. In: Pacific-Asia conference on knowledge discovery and data mining. Springer, pp 412–413

  55. Laurson Ü, Maggi FM (2016) A tool for the analysis of DMN decision tables. In: BPM (Demos), pp 56–60

  56. Batoulis K, Weske M (2017) A tool for checking soundness of decision-aware business processes. In: BPM (Demos), pp 1–5

  57. Corea C, Blatt J, Delfmann P (2019) A tool for decision logic verification in DMN decision tables. In: Proceedings of the Dissertation Award, Doctoral Consortium, and Demonstration Track at BPM 2019 co-located with 17th international conference on business process management, BPM 2019, Vienna, Austria, September 1–6, 2019

  58. Calvanese D, Dumas M, Maggi FM, Montali M (2017) Semantic DMN: formalizing decision models with domain knowledge. In: International joint conference on rules and reasoning. Springer, pp 70–86

  59. Hasić F, Serral Asensio E (2019) Executing IoT processes in BPMN 2.0: current support and remaining challenges. IEEE RCIS 2019 proceedings

  60. Janiesch C, Koschmider A, Mecella M, Weber B, Burattin A, Ciccio CD, Gal A, Kannengiesser U, Mannhardt F, Mendling J, Oberweis A, Reichert M, Rinderle-Ma S, Song W, Su J, Torres V, Weidlich M, Weske M, Zhang L. The internet-of-things meets business process management: mutual benefits and challenges, CoRR arXiv:1709.03628

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Acknowledgements

This work was in part supported by the Deutsche Forschungsgemeinschaft (Grant DE 1983/9-1).

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Correspondence to Faruk Hasić.

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Appendix

Appendix

This appendix gives an overview of the DMN metamodel, as specified in the latest DMN standard specification [1].

See Figs. 17, 18, 19, 20, 21, 22 and 23.

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Overview of the DMN meta model

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Decision table meta model

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Decision meta model

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Input data meta model

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Business knowledge model meta model

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Decision service meta model

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Definitions meta model

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Hasić, F., Corea, C., Blatt, J. et al. Decision model change patterns for dynamic system evolution. Knowl Inf Syst 62, 3665–3696 (2020). https://doi.org/10.1007/s10115-020-01469-w

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