Probability Based Heuristic for Predictive Business Process Monitoring

Böhmer, Kristof; Rinderle-Ma, Stefanie

doi:10.1007/978-3-030-02610-3_5

Cited by 6 publications

(1 citation statement)

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“…competitions in executing processes can be discovered and avoided through timely resource allocation decisions that are made based on the result of accurate next activity prediction. As a result, a lot of next activity prediction models have been proposed in the past decades, such as transition system and probabilistic models (Bhmer & Rinderle, 2018;Lakshmanan et al, 2015;Pauwels & Calders, 2020). Most of these researches, however, require explicit modelling of event logs with relevant expertise of BPs, and they lack the ability to characterize log data with intricate structures.…”

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confidence: 99%

Next activity prediction of ongoing business processes based on deep learning

et al. 2023

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Next activity prediction of business processes (BPs) provides valid execution information of ongoing (i.e., unfinished) process instances, which enables process executors to rationally allocate resources and detect process deviations in advance. Current researches on next activity prediction, however, concentrate mostly on model construction without in‐depth analysis of historical event logs. In this article, we are dedicated to proposing an approach to forecast the next activity effectively in BPs. After in‐depth analysis of historical event logs, three types of candidate activity attributes are defined and calculated as additional input for the prediction based on three essential elements, that is, frequent activity patterns, trace similarity and position information. Furthermore, we construct an effective hybrid prediction model combining the popular convolutional neural network (CNN) and bidirectional long short‐term memory (Bi‐LSTM) with self‐attention mechanism. Specifically, CNN is used to extract the temporal features before importing into Bi‐LSTM for accurate prediction, and self‐attention mechanism is applied to strengthen features that have decisive effects on the prediction results. Comparison experiments on four real‐life datasets demonstrate that our hybrid model with selected attributes achieves better performance on next activity prediction than single models, and improves the prediction accuracy by 2.98%, 6.05%, 2.70% and 5.26% on Helpdesk, Sepsis, BPIC2013 Incidents and BPIC2012O datasets than the state‐of‐the‐art methods, respectively.

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confidence: 99%