Abstract
With the increasing use of business process model management techniques, a large number of business process models are being developed in the industry, so the corresponding enterprises and organizations usually need to maintain a large business process set. An approach is presented based on the Meta-model for process model registration (MFI-5) to accurately measure the similarity of process models. First, based on MFI-5, the Process Model Description Framework (PMDF) is constructed. According to PMDF, a similarity feature set of the process model (SFS) is defined. Second, the Business Process Modeling Notation (BPMN) is utilized to describe corresponding business process, and the BPMN models are obtained. Further the BPMN models are identified and quantified by using SFS, so the model vectors are obtained. At last, the Tanimoto Coefficient-based algorithm is utilized to calculate the similarity between any two vectors, the similarity measure matrix of the BPMN models can be extracted. We illustrate the approach in the context of measuring the similarity of the online sales service processes, the result of which shows that the proposed approach can facilitate business process recommendation.
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
Similar content being viewed by others
References
Wang, J., Yin, J., Dou, W.: Business process management technology preface. J. Softw. 26(3), 447–448 (2015)
Yan, Z., Dijkman, R.: Paul Grefen.: Fast business process similarity search. Distrib. Parallel Databases 30(2), 105–144 (2012)
ISO/IEC-19763-5. Metamodel framework for interoperability (MFI)-part 5: metamodel for process model registration. https://www.iso.org/standard/53761.html. Accessed 15 May 2018
Comax, M., Chessa, S., Rieu, D., et al.: Evaluating the appropriateness of the BPMN 2.0 standard for modeling service choreographies: using an extended quality framework. Softw. Syst. Model. 15(1), 1–37 (2016)
Cao, B., Wang, J., Fan, J., et al.: Mapping elements with the hungarian algorithm: an efficient method for querying business process models. In: International Conference on Web Services (ICWS), pp. 129–136. IEEE, New York (2015)
Akkiraju, R., Ivan, A.: Discovering business process similarities: an empirical study with SAP best practice business processes. In: Maglio, P.P., Weske, M., Yang, J., Fantinato, M. (eds.) ICSOC 2010. LNCS, vol. 6470, pp. 515–526. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-17358-5_35
Dijkman, R., Dumas, M., Van Dongen, B., et al.: Similarity of business process models: metrics and evaluation. Inf. Syst. 36(2), 498–516 (2011)
Ehrig, M., Koschmider, A., Oberweis, A.: Measuring similarity between semantic business process models. In: International Conference on Conceptual Modeling, pp. 71–80. Springer, Heidelberg (2007)
Yan, Z., Dijkman, R., Grefen, P.: Fast business process similarity search with feature-based similarity estimation. In: Meersman, R., Dillon, T., Herrero, P. (eds.) OTM 2010. LNCS, vol. 6426, pp. 60–77. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-16934-2_8
Cao, B., Yin, J., Li, Y., et al.: A maximal common subgraph-based method for process retrieval. In: IEEE 20th International Conference on Web Services (ICWS), pp. 316–323. IEEE, New York (2013)
Huang, H., Peng, R., Feng, Z.: Efficient and exact query of large process model repositories in cloud workflow systems. IEEE Trans. Serv. Comput. (2015). https://doi.org/10.1109/tsc.2015.2481409, https://ieeexplore.ieee.org/document/7274764/
Lu, Y., Yu, H., Ming, Z., Wang, H.: A similarity measurement based on structure of business process. In: IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD), pp. 498–503. IEEE, New York (2016)
Zha, H., Wang, J., Wen, L., et al.: A workflow net similarity measure based on transition adjacency relations. Comput. Ind. 61(5), 463–471 (2010)
Jin, T., Wang, J., Wen, L.: Querying business process models based on semantics. In: Yu, J.X., Kim, M.H., Unland, R. (eds.) DASFAA 2011. LNCS, vol. 6588, pp. 164–178. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20152-3_13
Jin, T., Wang, J., Wen, L.: Efficient retrieval of similar workflow models based on behavior. In: Sheng, Quan Z., Wang, G., Jensen, Christian S., Xu, G. (eds.) APWeb 2012. LNCS, vol. 7235, pp. 677–684. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29253-8_64
Grigori, D., Corrales, C., Bouzeghoub, M., et al.: Ranking BPEL processes for service discovery. IEEE Trans. Serv. Comput. 3(3), 178–192 (2010)
Cheikhrouhou, S., Kallel, S., Jmaiel, M.: Toward a time-centric modeling of business processes models. In: IEEE 23rd International WETICE Conference, pp. 326–331. IEEE, New York (2014)
Geiger, M., Harrera, S., Lenhard, J., et al.: BPMN 2.0: the state of support and implementation. Future Gener. Comput. Syst. 80(3), 250–262 (2017)
Li, Z., Zhou, X., Keli, W., et al.: BPMN formalization based on extended petri nets model. Comput. Sci. 43(11), 40–48 (2016)
Syukriilah, N., Kusumo, D., Widowati, S.: Structural similarity analysis of business process model using selective reduce based on Petri Net. In: 3rd International Conference on Information and Communication Technology (ICoICT), pp. 1–5. IEEE, New York (2015)
Brocke, J., Rosemann, M.: Handbook on Business Process Management 1. 1st edn. Springer, Heidelberg (2010)
Xue, Z., Man, J., Zhang, C., et al.: Research on adaptability of BPMN based workflow execution process. Inf. Secur. Technol. 7(5), 56–58 (2016)
Qiao, M., Akkiraju, R., Rembert, A.J.: Towards efficient business process clustering and retrieval: combining language modeling and structure matching. In: Rinderle-Ma, S., Toumani, F., Wolf, K. (eds.) BPM 2011. LNCS, vol. 6896, pp. 199–214. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23059-2_17
Appice, A., Malerba, D.: A co-training strategy for multiple view clustering in process mining. IEEE Trans. Serv. Comput. 9(6), 832–845 (2016)
Kumar, A., Gupta, S., Singh, K., et al.: Comparison of various metrics used in collaborative filtering for recommendation system. In: 8th International Conference on Contemporary Computing (IC3), pp. 150–154. IEEE, New York (2015)
Acknowledgment
This work was supported by the National Key Research and Development Program of China (2016YFC0802500, 2016YFB0800403); the National Natural Science Foundation of China (61562073); the Humanities and Social Sciences Planning Fund of Ministry of Education (20171304); the Hubei Provincial Natural Science Foundation of China (2018CFC852); and the Natural Science Foundation of Hubei Provincial Department of Education (B2015240).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Li, Z. et al. (2018). MFI-5 Based Similarity Measurement of Business Process Models. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11063. Springer, Cham. https://doi.org/10.1007/978-3-030-00006-6_59
Download citation
DOI: https://doi.org/10.1007/978-3-030-00006-6_59
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00005-9
Online ISBN: 978-3-030-00006-6
eBook Packages: Computer ScienceComputer Science (R0)