Abstract
Social network mining aims at discovering and visualizing information exchange of resources and relations of resources among each other. For this, most existing approaches consider event logs as input data and therefore only depict how work was performed (as-is) and neglect information on how work should be performed (to-be), i.e., whether or not the actual execution is in compliance with the execution specified by the company or law. To bridge this gap, the presented approach considers event logs and natural language texts as input outlining rules on how resources are supposed to work together and which information may be exchanged between them. For pre-processing the natural language texts the large language model GPT-4 is utilized and its output is fed into a customized organizational mining component which delivers the to-be organizational perspective. In addition, we integrate well-known process discovery techniques from event logs to gather the as-is perspective. A comparison in the form of a graphical representation of both, the to-be and as-is perspectives, enables users to detect deviating behavior. The approach is evaluated based on a set of well-established process descriptions as well as synthetic and real-world event logs.
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.
The detailed results are available at https://www.cs.cit.tum.de/bpm/data/.
- 2.
https://pm4py.fit.fraunhofer.de, last access: 2023–07–06.
References
van der Aalst, W.M.P., Song, M.: Mining social networks: uncovering interaction patterns in business processes. In: Business Process Management, pp. 244–260 (2004). https://doi.org/10.1007/978-3-540-25970-1_16
van der Aalst, W.M.P., Weijters, T., Maruster, L.: Workflow mining: discovering process models from event logs. IEEE Trans. Knowl. Data Eng. 16(9), 1128–1142 (2004). https://doi.org/10.1109/TKDE.2004.47
Abdelkafi, M., Mbarek, N., Bouzguenda, L.: Mining organizational structures from email logs: an NLP based approach. In: Knowledge-Based and Intelligent Information & Engineering Systems, pp. 348–356 (2021). https://doi.org/10.1016/j.procs.2021.08.036
Appice, A.: Towards mining the organizational structure of a dynamic event scenario. J. Intell. Inf. Syst. 50(1), 165–193 (2017). https://doi.org/10.1007/s10844-017-0451-x
Barrientos, M., Winter, K., Mangler, J., Rinderle-Ma, S.: Verification of quantitative temporal compliance requirements in process descriptions over event logs. In: Advanced Information Systems Engineering, pp. 417–433 (2023). https://doi.org/10.1007/978-3-031-34560-9_25
Bauer, D., Longley, T., Ma, Y., Wilson, T.: NLP in human rights research - extracting knowledge graphs about police and army units and their commanders. CoRR (2022). https://arxiv.org/abs/2201.05230
Bellan, P., Dragoni, M., Ghidini, C.: Extracting business process entities and relations from text using pre-trained language models and in-context learning. In: Enterprise Design, Operations, and Computing, pp. 182–199 (2022). https://doi.org/10.1007/978-3-031-17604-3_11
Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Comput. Netw. ISDN Syst. 30(1), 107–117 (1998). https://doi.org/10.1016/S0169-7552(98)00110-X
Busch, P., Fettke, P.: Business process management under the microscope: the potential of social network analysis. In: Hawaii International Conference on System Sciences (2011). https://doi.org/10.1109/HICSS.2011.93
Dessì, D., Osborne, F., Reforgiato Recupero, D., Buscaldi, D., Motta, E.: Generating knowledge graphs by employing natural language processing and machine learning techniques within the scholarly domain. Future Gener. Comput. Syst. 116, 253–264 (2021). https://doi.org/10.1016/j.future.2020.10.026
Ebrahim, M., Golpayegani, S.A.H.: Anomaly detection in business processes logs using social network analysis. J. Comput. Virol. Hacking Tech. 18(2), 127–139 (2022). https://doi.org/10.1007/s11416-021-00398-8
Friedrich, F., Mendling, J., Puhlmann, F.: Process model generation from natural language text. In: Mouratidis, H., Rolland, C. (eds.) CAiSE 2011. LNCS, vol. 6741, pp. 482–496. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21640-4_36
Gao, A., Yang, Y., Zeng, M., Zhang, J., Wang, Y.: Organizational structure mining based on workflow logs. In: Business Intelligence: Artificial Intelligence in Business, Industry and Engineering, pp. 455–459 (2009). https://doi.org/10.1109/BIFE.2009.109
Ly, L.T., Rinderle, S., Dadam, P., Reichert, M.: Mining staff assignment rules from event-based data. In: Business Process Management Workshops, vol. 3812, pp. 177–190 (2005). https://doi.org/10.1007/11678564_16
Mangler, J., Rinderle-Ma, S.: Cloud process execution engine: architecture and interfaces (2022). https://doi.org/10.48550/ARXIV.2208.12214
Mustroph, H., Barrientos, M., Winter, K., Rinderle-Ma, S.: Verifying resource compliance requirements from natural language text over event logs. In: Business Process Management (2023). https://doi.org/10.1007/978-3-031-41620-0_15
Ni, Z., Wang, S., Li, H.: Mining organizational structure from workflow logs. In: Proceeding of the International Conference on e-Education, Entertainment and e-Management, pp. 222–225 (2011). https://doi.org/10.1109/ICeEEM.2011.6137791
OpenAI: GPT-4 Technical report. CoRR abs/2303.08774 (2023). https://doi.org/10.48550/arXiv.2303.08774
Pika, A., Leyer, M., Wynn, M.T., Fidge, C.J., ter Hofstede, A.H.M., van der Aalst, W.M.P.: Mining resource profiles from event logs. ACM Trans. Manag. Inf. Syst. 8(1), 1:1–1:30 (2017). https://doi.org/10.1145/3041218
Qin, S., Xu, C., Zhang, F., Jiang, T., Ge, W., Li, J.: Research on application of Chinese natural language processing in constructing knowledge graph of chronic diseases. In: 2021 International Conference on Communications, Information System and Computer Engineering (CISCE), pp. 271–274 (2021). https://doi.org/10.1109/CISCE52179.2021.9445976
Raitubu, N., Sungkono, K.R., Sarno, R., Wahyuni, C.S.: Detection of bottleneck and social network in business process of agile development. In: 2019 International Seminar on Application for Technology of Information and Communication (iSemantic), pp. 208–213 (2019). https://doi.org/10.1109/ISEMANTIC.2019.8884341
Reijers, H.A., Song, M., Jeong, B.: Analysis of a collaborative workflow process with distributed actors. Inf. Syst. Front. 11(3), 307–322 (2009). https://doi.org/10.1007/s10796-008-9092-5
Sellami, R., Gaaloul, W., Moalla, S.: An ontology for workflow organizational model mining. In: Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises, pp. 199–204 (2012). https://doi.org/10.1109/WETICE.2012.29
Song, M., van der Aalst, W.M.P.: Towards comprehensive support for organizational mining. Decis. Support Syst. 46(1), 300–317 (2008). https://doi.org/10.1016/j.dss.2008.07.002
Tao, J., Deokar, A.V.: An organizational mining approach based on behavioral process patterns. In: Americas Conference on Information Systems. Association for Information Systems (2014). http://aisel.aisnet.org/amcis2014/EndUserIS/GeneralPresentations/11
Yang, J., Ouyang, C., van der Aalst, W.M.P., ter Hofstede, A.H.M., Yu, Y.: OrdinoR: a framework for discovering, evaluating, and analyzing organizational models using event logs. Decis. Support Syst. 158, 113771 (2022). https://doi.org/10.1016/j.dss.2022.113771
Zhao, W., Zhao, X.: Process mining from the organizational perspective. Adv. Intell. Syst. Comput. 277, 701–708 (2014). https://doi.org/10.1007/978-3-642-54924-3_66
Acknowledgements
This work has been partly funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – project number 514769482.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Mustroph, H., Winter, K., Rinderle-Ma, S. (2024). Social Network Mining from Natural Language Text and Event Logs for Compliance Deviation Detection. In: Sellami, M., Vidal, ME., van Dongen, B., Gaaloul, W., Panetto, H. (eds) Cooperative Information Systems. CoopIS 2023. Lecture Notes in Computer Science, vol 14353. Springer, Cham. https://doi.org/10.1007/978-3-031-46846-9_19
Download citation
DOI: https://doi.org/10.1007/978-3-031-46846-9_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-46845-2
Online ISBN: 978-3-031-46846-9
eBook Packages: Computer ScienceComputer Science (R0)