Abstract
AbstractProcess mining is a scientific discipline that analyzes event data, often collected in databases called event logs. Recently, uncertain event logs have become of interest, which contain non-deterministic and stochastic event attributes that may represent many possible real-life scenarios. In this paper, we present a method to reliably estimate the probability of each of such scenarios, allowing their analysis. Experiments show that the probabilities calculated with our method closely match the true chances of occurrence of specific outcomes, enabling more trustworthy analyses on uncertain data.
Publisher
Springer International Publishing
Reference11 articles.
1. van der Aa, H., Leopold, H., Weidlich, M.: Partial order resolution of event logs for process conformance checking. Decis. Support Syst. 136, 113347 (2020). https://doi.org/10.48550/arXiv.2007.02416
2. Ao, X., Luo, P., Li, C., Zhuang, F., He, Q.: Online frequent episode mining. In: 2015 IEEE 31st International Conference on Data Engineering. IEEE (2015). https://doi.org/10.1109/ICDE.2015.7113342
3. Berti, A., van Zelst, S.J., van der Aalst, W.M.P.: Process mining for Python (PM4Py): bridging the gap between process- and data science. In: ICPM Demo Track (CEUR 2374) (2019)
4. Busany, N., van der Aa, H., Senderovich, A., Gal, A., Weidlich, M.: Interval-based queries over lossy IoT event streams. Trans. Data Sci. 1(4), 1–27 (2020). https://doi.org/10.1145/3385191
5. Lecture Notes in Computer Science;B van Dongen,2017
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献