(one tenth of the training sample size). Table 3 shows many results with validation precision close to or in excess of 0 . 8. While we have no comparison to the state-of-the-art on these datasets by Breuker et al. (2016), this level of precision is encouraging for practical applications. It is also generally above the levels reported in related works (see Section 2), although this must be interpreted with caution due to different datasets with possibly very differ- ent characteristics, such as number of unique events, trace length, or the patterns due to the structure of the underlying, generating process. Prediction is significantly better for the BPI 2012 W dataset with all events com- pared to the same dataset with completion events only as it exploits the regularity that after every ”Start” event for an activity, the corresponding ”Complete” event for that activity follows. Including the organizational resource or group in the predicting data provides additional information that should lead to improved prediction precision. Including this information in both predictor and predictand allows prediction not only of the activity of the next process event but also of the resource associated with the next process event. However, this comes at the cost of a larger vocabulary and should lead to lower prediction precision. We explore both options, and the results in Table 4 are generally as expected. Including the organizational group or resource information only in the predictor (Table 4) improves the training precision for all datasets, by up to 0 . 07, compared to the baseline in Table 3. However, the validation precision does not follow the same pattern, improving only for some datasets, and even then improving to a lesser extent as the training precision. This suggests that including resource information for prediction may be useful but also runs the danger of overfitting the model. 6.2. The Effect of Resource Information 19