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2024 | OriginalPaper | Chapter

Abstractions, Scenarios, and Prompt Definitions for Process Mining with LLMs: A Case Study

Authors : Alessandro Berti, Daniel Schuster, Wil M. P. van der Aalst

Published in: Business Process Management Workshops

Publisher: Springer Nature Switzerland

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Abstract

Large Language Models (LLMs) are capable of answering questions in natural language for various purposes. With recent advancements (such as GPT-4), LLMs perform at a level comparable to humans for many proficient tasks. The analysis of business processes could benefit from a natural process querying language and using the domain knowledge on which LLMs have been trained. However, it is impossible to provide a complete database or event log as an input prompt due to size constraints. In this paper, we apply LLMs in the context of process mining by i) abstracting the information of standard process mining artifacts and ii) describing the prompting strategies. We implement the proposed abstraction techniques into pm4py, an open-source process mining library. We present a case study using available event logs. Starting from different abstractions and analysis questions, we formulate prompts and evaluate the quality of the answers.

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previous chapter Equivalence of Data Petri Nets with Arithmetic

next chapter Text-Aware Predictive Process Monitoring of Knowledge-Intensive Processes: Does Control Flow Matter?

https://pm4py.fit.fraunhofer.de.

https://data.4tu.nl/articles/_/12683249/1.

https://data.4tu.nl/collections/_/5065541/1.

https://data.4tu.nl/articles/_/12707639/1.

van der Aa, H., Rebmann, A., Leopold, H.: Natural language-based detection of semantic execution anomalies in event logs. Inf. Syst. 102, 101824 (2021)CrossRef

Baader, G., Krcmar, H.: Reducing false positives in fraud detection: combining the red flag approach with process mining. Int. J. Account. Inf. Syst. 31, 1–16 (2018)CrossRef

Baier, T., Mendling, J., Weske, M.: Bridging abstraction layers in process mining. Inf. Syst. 46, 123–139 (2014)CrossRef

Barbieri, L., Madeira, E., Stroeh, K., van der Aalst, W.M.P.: A natural language querying interface for process mining. J. Intell. Inf. Syst. 61, 1–30 (2022)

Berti, A., van der Aalst, W.M.P.: OC-PM: analyzing object-centric event logs and process models. Int. J. Softw. Tools Technol. Transf. 25(1), 1–17 (2023)CrossRef

Berti, A., van Zelst, S., Schuster, D.: PM4Py: a process mining library for python. Softw. Impacts 17, 100556 (2023). https://www.sciencedirect.com/science/article/pii/S2665963823000933

Brown, T., et al.: Language models are few-shot learners. Adv. Neural. Inf. Process. Syst. 33, 1877–1901 (2020)

Busch, K., Rochlitzer, A., Sola, D., Leopold, H.: Just tell me: prompt engineering in business process management. In: van der Aa, H., Bork, D., Proper, H.A., Schmidt, R. (eds.) BPMDS 2023. LNBIP, vol. 479, pp. 3–11. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-34241-7_1CrossRef

Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)

10.

Dixit, P.M., Buijs, J.C.A.M., van der Aalst, W.M.P., Hompes, B., Buurman, H.: Enhancing process mining results using domain knowledge. In: Proceedings of the 5th International Symposium on Data-Driven Process Discovery and Analysis (SIMPDA 2015), pp. 79–94. CEUR-WS.org (2015)

11.

Esser, S., Fahland, D.: Multi-dimensional event data in graph databases. J. Data Semant. 10(1–2), 109–141 (2021)CrossRef

12.

Kaplan, J., et al.: Scaling laws for neural language models. arXiv preprint arXiv:2001.08361 (2020)

13.

Kecht, C., Egger, A., Kratsch, W., Röglinger, M.: Quantifying chatbots’ ability to learn business processes. Inf. Syst. 113, 102176 (2023)CrossRef

14.

Kobeissi, M., Assy, N., Gaaloul, W., Defude, B., Benatallah, B., Haidar, B.: Natural language querying of process execution data. Inf. Syst. 116, 102227 (2023)CrossRef

15.

Liu, A., Hu, X., Wen, L., Yu, P.S.: A comprehensive evaluation of ChatGPT’s zero-shot text-to-SQL capability. CoRR abs/2303.13547 (2023)

16.

de Murillas, E.G.L., Reijers, H.A., van der Aalst, W.M.P.: Connecting databases with process mining: a meta model and toolset. Softw. Syst. Model. 18(2), 1209–1247 (2019)CrossRef

17.

OpenAI: GPT-4 technical report. CoRR abs/2303.08774 (2023)

18.

Polyvyanyy, A.: Process Querying Methods. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-92875-9CrossRef

19.

Polyvyanyy, A., Ouyang, C., Barros, A., van der Aalst, W.M.P.: Process querying: enabling business intelligence through query-based process analytics. Decis. Support Syst. 100, 41–56 (2017)CrossRef

20.

Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. OpenAI Blog 1(8), 9 (2019)

21.

Reimers, N., Gurevych, I.: Sentence-bert: sentence embeddings using siamese bert-networks. arXiv preprint arXiv:1908.10084 (2019)

22.

Schönig, S., Rogge-Solti, A., Cabanillas, C., Jablonski, S., Mendling, J.: Efficient and customisable declarative process mining with SQL. In: Nurcan, S., Soffer, P., Bajec, M., Eder, J. (eds.) CAiSE 2016. LNCS, vol. 9694, pp. 290–305. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-39696-5_18CrossRef

23.

Schuster, D., van Zelst, S.J., van der Aalst, W.M.P.: Utilizing domain knowledge in data-driven process discovery: a literature review. Comput. Ind. 137, 103612 (2022)CrossRef

24.

Vogelgesang, T., Ambrosy, J., Becher, D., Seilbeck, R., Geyer-Klingeberg, J., Klenk, M.: Celonis PQL: a query language for process mining. In: Polyvyanyy, A. (ed.) Process Querying Methods, pp. 377–408. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-92875-9_13CrossRef

25.

Weerdt, J.D., Wynn, M.T.: Foundations of process event data. In: van der Aalst, W.M.P., Carmona, J. (eds.) Process Mining Handbook. LNBIP, vol. 448, pp. 193–211. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-08848-3_6CrossRef

26.

Winter, K., Rinderle-Ma, S.: Detecting constraints and their relations from regulatory documents using NLP techniques. In: Panetto, H., Debruyne, C., Proper, H.A., Ardagna, C.A., Roman, D., Meersman, R. (eds.) OTM 2018. LNCS, vol. 11229, pp. 261–278. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-02610-3_15CrossRef

Title: Abstractions, Scenarios, and Prompt Definitions for Process Mining with LLMs: A Case Study
Authors: Alessandro Berti
Daniel Schuster
Wil M. P. van der Aalst
Publisher: Springer Nature Switzerland
Book: Business Process Management Workshops
Print ISBN: 978-3-031-50973-5

Electronic ISBN: 978-3-031-50974-2

Copyright Year: 2024
DOI: https://doi.org/10.1007/978-3-031-50974-2_32

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