Abstract
In predictive process analytics, current and historical process data in event logs are used to predict future. E.g., to predict the next activity or how long a process will still require to complete. Recurrent neural networks (RNN) and its subclasses have been demonstrated to be well suited for creating prediction models. Thus far, event attributes have not been fully utilized in these models. The biggest challenge in exploiting them in prediction models is the potentially large amount of event attributes and attribute values. We present a novel clustering technique which allows for trade-offs between prediction accuracy and the time needed for model training and prediction. As an additional finding, we also find that this clustering method combined with having raw event attribute values in some cases provides even better prediction accuracy at the cost of additional time required for training and prediction.
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Acknowledgments
We want to thank QPR Software Plc for funding our research. Financial support of Academy of Finland project 313469 is acknowledged.
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Hinkka, M., Lehto, T., Heljanko, K. (2019). Exploiting Event Log Event Attributes in RNN Based Prediction. In: Welzer, T., et al. New Trends in Databases and Information Systems. ADBIS 2019. Communications in Computer and Information Science, vol 1064. Springer, Cham. https://doi.org/10.1007/978-3-030-30278-8_40
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DOI: https://doi.org/10.1007/978-3-030-30278-8_40
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