Abstract
The execution of a business process is often determined by the surrounding context, e.g., department, product, or other attributes an event provides. Process discovery mainly focuses on the executed activities, although the context of a case may be needed to accurately represent a process instance, e.g., for clustering, prediction, or anomaly detection. Hence, in this paper, we present a representation learning technique (Case2vec) using word embeddings for business process data to better encode process instances. Our work extends Trace2vec and incorporates an additional semantic level by using not only the activity name but also the attributes and thereby incorporating the context. We evaluate our approach in the context of trace clustering. Additionally, we show that Case2vec can be used to abstract events which are semantically similar but syntactically different. We also show that word embeddings allow for interpretability when employing vector space arithmetic.
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.
Source code publicly available at: https://github.com/alexsee/case2vec.
References
Bose, R.P.J.C., van der Aalst, W.M.P.: Context aware trace clustering: towards improving process mining results. In: International Conference on Data Mining (SIAM) (2009)
Bui, H.-N., Vu, T.-S., Nguyen, H.-H., Nguyen, T.-T., Ha, Q.-T.: Exploiting CBOW and LSTM models to generate trace representation for process mining. In: Sitek, P., Pietranik, M., Krótkiewicz, M., Srinilta, C. (eds.) ACIIDS 2020. CCIS, vol. 1178, pp. 35–46. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-3380-8_4
Camargo, M., Dumas, M., González-Rojas, O.: Learning accurate LSTM models of business processes. In: Hildebrandt, T., van Dongen, B.F., Röglinger, M., Mendling, J. (eds.) BPM 2019. LNCS, vol. 11675, pp. 286–302. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-26619-6_19
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
van Dongen, B.: BPI Challenge 2015. https://doi.org/10.4121/uuid:31a308ef-c844-48da-948c-305d167a0ec1
van Dongen, B.: BPI Challenge 2019. https://doi.org/10.4121/uuid:d06aff4b-79f0-45e6-8ec8-e19730c248f1
Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: International Conference on Machine Learning (2014)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems (2013)
Nolle, T., Luettgen, S., Seeliger, A., Mühlhäuser, M.: BINet: multi-perspective business process anomaly classification. Inf. Syst. (2019)
Nolle, T., Seeliger, A., Mühlhäuser, M.: BINet: multivariate business process anomaly detection using deep learning. In: Weske, M., Montali, M., Weber, I., vom Brocke, J. (eds.) BPM 2018. LNCS, vol. 11080, pp. 271–287. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98648-7_16
Song, M., Yang, H., Siadat, S.H., Pechenizkiy, M.: A comparative study of dimensionality reduction techniques to enhance trace clustering performances. Expert Syst. Appl. 40(9), 3722–3737 (2013)
Song, M., Günther, C.W., van der Aalst, W.M.P.: Trace clustering in process mining. In: Ardagna, D., Mecella, M., Yang, J. (eds.) BPM 2008. LNBIP, vol. 17, pp. 109–120. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-00328-8_11
Tavares, G.M., Barbon, S.: Analysis of language inspired trace representation for anomaly detection. In: Bellatreche, L., et al. (eds.) TPDL/ADBIS -2020. CCIS, vol. 1260, pp. 296–308. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-55814-7_25
Tax, N., Sidorova, N., Haakma, R., van der Aalst, W.M.P.: Event abstraction for process mining using supervised learning techniques. In: Bi, Y., Kapoor, S., Bhatia, R. (eds.) IntelliSys 2016. LNNS, vol. 15, pp. 251–269. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-56994-9_18
Acknowledgment
This work is funded by the German Federal Ministry of Education and Research (BMBF) research project KI.RPA [01IS18022D].
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Luettgen, S., Seeliger, A., Nolle, T., Mühlhäuser, M. (2021). Case2vec: Advances in Representation Learning for Business Processes. In: Leemans, S., Leopold, H. (eds) Process Mining Workshops. ICPM 2020. Lecture Notes in Business Information Processing, vol 406. Springer, Cham. https://doi.org/10.1007/978-3-030-72693-5_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-72693-5_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-72692-8
Online ISBN: 978-3-030-72693-5
eBook Packages: Computer ScienceComputer Science (R0)