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Comparative evaluation of encoding techniques for workflow process remaining time prediction for cloud applications
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Orientador(es)
Resumo(s)
With the increasing adoption of cloud infrastructure for workflow process deployment, the workflow process remaining time prediction has received more attention due to its significance in improving response time and optimizing resource allocation. Event encoding techniques, by capturing the temporal dynamics and contextual dependencies of process events, improve the accuracy of workflow process remaining time predictions. However, there remains a lack of comprehensive empirical evaluation to analyze the impact of various event encoding techniques on prediction accuracy. To fill this gap, this paper conducts an extensive experimental evaluation of five state-of-the-art event encoding techniques, including One-Hot, Skip-Gram, CBOW (Continuous Bag-of-Word), FastText and GloVe, across nine prediction models based on LSTM (Long Short-Term Memory), GRU (Gated Recurrent Unit), and QRNN (Quasi-Recurrent Neural Network). The evaluation utilizes eight real-world event logs to assess the accuracy of workflow remaining time predictions. The experimental results demonstrate that the GloVe encoding technique consistently yields superior prediction accuracy across the majority of prediction models and event logs.
Descrição
Liu, C., Liu, W., Guo, N., Song, R., Gu, Y., Cheng, L., & Zeng, Q. (2025). Comparative evaluation of encoding techniques for workflow process remaining time prediction for cloud applications. Journal of Cloud Computing, 14, Article 36. https://doi.org/10.1186/s13677-025-00763-8 --- This work was partly supported by the National Key R&D Program of China under Grant 2022ZD0119501, National Natural Science Foundation of China under Grant 62472264 and 52374221, the Taishan Scholars Program of Shandong Province under Grants ts20190936 and Grant tsqn201909109, the Natural Science Excellent Youth Foundation of Shandong Province under Grant ZR2021YQ45, and the Youth Innovation Science and Technology Team Foundation of Shandong Higher School under Grant 2021KJ031.
Palavras-chave
Empirical evaluation Encoding techniques Process mining Remaining time prediction Software Computer Networks and Communications