# Exploiting Event Log Event Attributes in RNN Based Prediction

## TL;DR

This paper introduces a clustering technique to better utilize event log attributes in RNN-based predictive models, improving accuracy and efficiency in process prediction tasks.

## Contribution

It presents a novel clustering method that balances prediction accuracy with training and prediction time, enhancing RNN-based process analytics.

## Key findings

- Clustering improves prediction accuracy with attribute data.
- Combining raw attribute values with clustering can outperform pure clustering.
- Trade-offs exist between accuracy and computational time.

## Abstract

In predictive process analytics, current and historical process data in event logs is used to predict the 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 that 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.