Abstract
Deep learning models for next event prediction in predictive process monitoring have shown significant performance improvements over conventional methods. However, they are often criticized for being black-box models. Without allowing analysts to understand what such models have learned, it is difficult to establish trust in their abilities.
In this work, we propose a technique to infer a likelihood graph from a next event predictor (NEP) to capture and visualize its behavior. Our approach first generates complete cases, including event attributes, using the NEP. From this set of cases, a multi-perspective likelihood graph is inferred. Including event attributes in the graph allows analysts to better understand the learned decision and branching points of the process.
The results of the evaluation show that inferred graphs generalize beyond the event log, achieve high F-scores, and small likelihood deviations. We conclude black-box NEP can be used to generate conforming cases even for noisy event logs. As a result, our visualization technique, which represents exactly this set of cases, shows what the NEP has learned, thus mitigating one of their biggest criticisms.
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van Dongen, Boudewijn (2020): BPI Challenge 2020. 4TU.ResearchData. Collection. https://doi.org/10.4121/uuid:52fb97d4-4588-43c9-9d04-3604d4613b51.
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Gerlach, Y., Seeliger, A., Nolle, T., Mühlhäuser, M. (2022). Inferring a Multi-perspective Likelihood Graph from Black-Box Next Event Predictors. In: Franch, X., Poels, G., Gailly, F., Snoeck, M. (eds) Advanced Information Systems Engineering. CAiSE 2022. Lecture Notes in Computer Science, vol 13295. Springer, Cham. https://doi.org/10.1007/978-3-031-07472-1_2
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