concept implementation to demonstrate feasibility of applying NLP methods to prcoess prediction, and an experimental evaluation to demonstrate effectiveness and performance are clearly required. 

## 4. Implementation 

A number of software frameworks for deep-learning have become available re- cently (Bahrampour et al., 2015). We implement our approach using Tensorflow as it provides a suitable level of abstraction, provides RNN specific functionality, and can be used on high-performance parallel, cluster, and GPU computing platforms. Code, data and complete results are available from the corresponding author $^{3}$. 

Our network features an architecture as in Fig. 1 with two hidden RNN layers using LSTM cells. This architecture still affords many choices, in particular the dimensionality of the embedding space and the number of unrolled steps. 

The dimensionality of the embedding space must be guided by the vocabulary size (the number of unique event types). A larger dimensionality allows better sep- aration of words in that space, which likely leads to better predictive performance, but at the cost of computational effort. Our baseline in the experimental evaluation (Sec. 5) is 125, and we explore the effect of varying this parameter on predictive performance. The number of ”unrolled” steps indicates the length of the sequence of words across which state information is maintained, independent of vocabulary size. A larger number of unrolled steps allows the network to take earlier process events into account when predicting the following process event. This, too, is at the expense of computational effort. Our baseline for the experimental evaluation is 20, and we explore the effect of varying this parameter on predictive performance. 

In addition to these two parameters, a number of general neural network options 

