International Conference on Mining Intelligence and Knowledge Exploration

Mining Intelligence and Knowledge Exploration pp 115-129 | Cite as

A Graph Processing Based Approach for Automatic Detection of Semantic Inconsistency Between BPMN Process Model and SBVR Rules

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9468)

Abstract

Business Process Modeling Notation (BPMN) is a technique for graphically drawing and illustrating business processes in diagramtic form. Semantic of Business Vocabulary and Business Rules (SBVR) is a declarative language used to define business vocabulary, rules and policy. Several times inconsistencies occur between BPMN and SBVR as they are independently maintained. Our aim is to investigate techniques for automatically detecting inconsistencies between business process and rules. We present a method for inconsistency detection (between BPMN and SBVR) based on converting SBVR rules to graphical representation and apply sub graph-isomorphism to detect instances of inconsistencies between BPMN and SBVR models. We propose a multi-step process framework for identification of instances of inconsistencies between the two models. We first generate an XML of BPMN diagram and apply parsing and tag extraction. We then apply Stanford NLP Parser to generate parse tree of rules. The detailed information about the parse tree is stored in the form of Typed Dependency which represent grammatical relation between words of a sentence. We utilize the grammatical relation extract triplet (actor-action-object) of a sentence. We find node-induced sub-graph of all possible length of nodes of a graph and apply VF2 Algorithm to detect instances of inconsistency between sub graphs. Finally, we evaluate the proposed research framework by conducting experiments on synthetic dataset to validate the accuracy and effectiveness of our approach.

Keywords

Business Process Modeling Business rule modeling Inconsistency detection Business process intelligence Graph matching algorithms 

References

  1. 1.
    Cordella, L.P., Foggia, P., Sansone, C., Vento, M.: An improved algorithm for matching large graphs. In: 3rd IAPR-TC15 Workshop on Graph-based Representations in Pattern Recognition, Cuen, pp. 149–159 (2001)Google Scholar
  2. 2.
    Cordella, L., Foggia, P., Sansone, C., Vento, M.: A (sub)graph isomorphism algorithm for matching large graphs. Pattern Anal. Mach. Intell. IEEE Trans. 26(10), 1367–1372 (2004)CrossRefGoogle Scholar
  3. 3.
    Cordella, L.P., Foggia, P., Sansone, C., Vento, M.: Subgraph transformations for the inexact matching of attributed relational graphs. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  4. 4.
    Cordella, L.P., Foggia, P., Sansone, C., Vento, M.: Performance evaluation of the vf graph matching algorithm. In: Proceedings of the International Conference on Image Analysis and Processing, 1999, pp. 1172–1177. IEEE (1999)Google Scholar
  5. 5.
    Dali, L., Fortuna, B.: Triplet extraction from sentences using svm. In: Proceedings of SiKDD (2008)Google Scholar
  6. 6.
    Habich, D., Richly, S., Demuth, B., Gietl, F., Spilke, J., Lehner, W., Assmann, U.: Joining business rules and business processes. In: Proceedings of IT (2010)Google Scholar
  7. 7.
    Krogstie, J., McBrien, P., Owens, R., Seltveit, A.H.: Information systems development using a combination of process and rule based approaches. In: Andersen, R., Bubenko, J.A., Solvberg, A. (eds.) Advanced Information Systems Engineering. LNCS, vol. 498, pp. 319–335. Springer, Heidelberg (1991)CrossRefGoogle Scholar
  8. 8.
    Mickeviciute, E., Nemuraite, L., Butleris, R.: Applying SBVR business vocabulary and business rules for creating BPMN process models. In: Abramowicz, W., Kokkinaki, A. (eds.) BIS 2014 Workshops. LNBIP, vol. 183, pp. 105–116. Springer, Heidelberg (2014) Google Scholar
  9. 9.
    Myint, Z.T.T., Win, K.K.: Triple patterns extraction for accessing data on ontology. Int. J. Future Comput. Commun. 3(1), 40 (2014)CrossRefGoogle Scholar
  10. 10.
    Rusu, D., Dali, L., Fortuna, B., Grobelnik, M., Mladenic, D.: Triplet extraction from sentences. In: Proceedings of the 10th International Multiconference Information Society-IS, pp. 8–12 (2007)Google Scholar
  11. 11.
    Sharma, D.K., Prakash, N., Sharma, H., Singh, D.: Automatic construction of process template from business rule. In: 2014 Seventh International Conference on Contemporary Computing (IC3), pp. 419–424. IEEE (2014)Google Scholar
  12. 12.
    Skersys, T., Kapocius, K., Butleris, R., Danikauskas, T.: Extracting business vocabularies from business process models: Sbvr and bpnm standards-based approach. Comput. Sci. Inf. Syst. 11(4), 1515–1535 (2014)CrossRefGoogle Scholar
  13. 13.
    Skersys, T., Tutkute, L., Butleris, R., Butkiene, R.: Extending bpmn business process model with sbvr business vocabulary and rules. Inf. Technol. Control 41(4), 356–367 (2012)Google Scholar
  14. 14.
    Steen, B., Pires, L.F., Iacob, M.E.: Automatic generation of optimal business processes from business rules. In: 2010 14th IEEE International Enterprise Distributed Object Computing Conference Workshops (EDOCW), pp. 117–126. IEEE (2010)Google Scholar
  15. 15.
    Ullmann, J.R.: An algorithm for subgraph isomorphism. J. ACM 23, 31–42 (1976)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Zur Muehlen, M., Indulska, M., Kittel, K.: Towards integrated modeling of business processes and business rules. In: ACIS 2008 Proceedings, p. 108 (2008)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Indraprastha Institute of Information Technology-Delhi (IIIT-D)New DelhiIndia
  2. 2.Software Analytics Research Lab (SARL)New DelhiIndia

Personalised recommendations