Text-Aware Predictive Monitoring of Business Processes
This addresses a gap in business process monitoring for industries where textual information is critical, though it is incremental as it extends existing LSTM and NLP techniques to a specific domain.
The paper tackles the problem of predicting business processes by incorporating textual data from event logs, which existing methods largely ignore, and demonstrates that their text-aware LSTM-based model outperforms state-of-the-art methods on simulated and real-world logs.
The real-time prediction of business processes using historical event data is an important capability of modern business process monitoring systems. Existing process prediction methods are able to also exploit the data perspective of recorded events, in addition to the control-flow perspective. However, while well-structured numerical or categorical attributes are considered in many prediction techniques, almost no technique is able to utilize text documents written in natural language, which can hold information critical to the prediction task. In this paper, we illustrate the design, implementation, and evaluation of a novel text-aware process prediction model based on Long Short-Term Memory (LSTM) neural networks and natural language models. The proposed model can take categorical, numerical and textual attributes in event data into account to predict the activity and timestamp of the next event, the outcome, and the cycle time of a running process instance. Experiments show that the text-aware model is able to outperform state-of-the-art process prediction methods on simulated and real-world event logs containing textual data.