Text-Aware Predictive Monitoring of Business Processes
Marco Pegoraro, Merih Seran Uysal, David Benedikt Georgi, Wil, M.P. van der Aalst

TL;DR
This paper introduces a novel text-aware process prediction model that leverages natural language data in event logs, significantly improving prediction accuracy over existing methods.
Contribution
The paper presents a new LSTM-based model that incorporates textual, categorical, and numerical data for real-time business process prediction.
Findings
Outperforms state-of-the-art methods on real-world logs
Effectively utilizes natural language information in event data
Predicts activity, timestamp, outcome, and cycle time accurately
Abstract
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…
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