Abstract
Today’s organizations often have to manage hundreds of process models. This requires organizations to be able to efficiently manage process models as a kind of organizational data. Most of previous approaches for process model representation exploit graph data structures to represent (part of) the process control-flow. While these representations work well to analyze the overall process behavior and its KPIs, they are complex data structures which pose some challenges for other kind of analyses requiring, e.g., model comparison. In this paper we explore an alternative approach to representing process structure. We introduce a set of features that describe the context of an activity inside a process, thus embedding the information about the process structure in the activity representation. This representation enables the use of standard vector techniques to capture certain structural similarities among activities and is then suited to support tasks like similarity-based comparison and mapping of processes without resorting to graph-based approaches.
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van der Aalst, W., et al.: Workflow mining: discovering process models from event logs. IEEE Trans. Knowl. Data Eng. 16(9), 1128–1142 (2004)
Barbon Junior, S., Ceravolo, P., Damiani, E., Marques Tavares, G.: Evaluating trace encoding methods in process mining. In: Bowles, J., Broccia, G., Nanni, M. (eds.) DataMod 2020. LNCS, vol. 12611, pp. 174–189. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-70650-0_11
Bose, R.P.J.C., et al.: Context aware trace clustering: towards improving process mining results, pp. 401–412 (2009)
Chiorrini, A., et al.: Exploiting instance graphs and graph neural networks for next activity prediction. In: Munoz-Gama, J., et al. (eds.) Process Mining Workshops, pp. 115–126. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-98581-3_9
Corradini, F., et al.: RePROSitory: a repository platform for sharing business PROcess modelS. 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, vol. 2420, pp. 149–153. CEUR-WS (2019)
De Koninck, P., vanden Broucke, S., De Weerdt, J.: act2vec, trace2vec, log2vec, and model2vec: representation learning for business processes. In: Weske, M., Montali, M., Weber, I., vom Brocke, J. (eds.) BPM 2018. LNCS, vol. 11080, pp. 305–321. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98648-7_18
Diaz, J., et al.: Workflow hunt: combining keyword and semantic search in scientific workflow repositories, pp. 138–147 (2017)
Dijkman, R., et al.: Similarity of business process models: metrics and evaluation. Inf. Syst. 36(2), 498–516 (2011). Special Issue: Semantic Integration of Data, Multimedia, and Services
van Dongen, B.: BPI Challenge 2012 (2012). https://data.4tu.nl/articles/dataset/BPI_Challenge_2012/12689204
van Dongen, B.: BPI Challenge 2017 (2017). https://data.4tu.nl/articles/dataset/BPI_Challenge_2017/12696884
Hake, P., Zapp, M., Fettke, P., Loos, P.: Supporting business process modeling using RNNs for label classification. In: Frasincar, F., Ittoo, A., Nguyen, L.M., Métais, E. (eds.) NLDB 2017. LNCS, vol. 10260, pp. 283–286. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59569-6_35
Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Discovering block-structured process models from event logs - a constructive approach. In: Colom, J.-M., Desel, J. (eds.) PETRI NETS 2013. LNCS, vol. 7927, pp. 311–329. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38697-8_17
Leontjeva, A., Conforti, R., Di Francescomarino, C., Dumas, M., Maggi, F.M.: Complex symbolic sequence encodings for predictive monitoring of business processes. In: Motahari-Nezhad, H.R., Recker, J., Weidlich, M. (eds.) BPM 2015. LNCS, vol. 9253, pp. 297–313. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23063-4_21
Pasquadibisceglie, V., et al.: A multi-view deep learning approach for predictive business process monitoring. IEEE Trans. Serv. Comput. (2021)
Polyvyanyy, A., Vanhatalo, J., Völzer, H.: Simplified computation and generalization of the refined process structure tree. In: Bravetti, M., Bultan, T. (eds.) WS-FM 2010. LNCS, vol. 6551, pp. 25–41. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-19589-1_2
Van Der Aalst, W.: Process Mining: Data Science in Action, vol. 2. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-49851-4
Venugopal, I., et al.: A comparison of deep learning methods for analysing and predicting business processes. In: Proceedings of International Joint Conference on Neural Networks, IJCNN (2021)
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Chiorrini, A., Diamantini, C., Genga, L., Pioli, M., Potena, D. (2022). Embedding Process Structure in Activities for Process Mapping and Comparison. In: Chiusano, S., et al. New Trends in Database and Information Systems. ADBIS 2022. Communications in Computer and Information Science, vol 1652. Springer, Cham. https://doi.org/10.1007/978-3-031-15743-1_12
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DOI: https://doi.org/10.1007/978-3-031-15743-1_12
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