Table 4: Precision when including resources or organizational groups Including the organizational resource or group in both the predictor and pre- dictand affects the prediction precision for the datasets in different ways. Training precision for the BPI 2013 Incident decreases, but increases for the BPI 2013 Prob- lem dataset. Validation precision drops significantly for both datasets. Training and validation precision for all BPI 2012 datasets also drop significantly. An extreme example is the BPI 2012 W dataset with completion events only, where training pre- cision drops to . 313 and validation precision to . 208. Here, the resources assigned to events do not follow any underlying regularity, impairing prediction performance. In contrast, for the BPI2012.W with all events, the resource of the completion event is the same as of the start event, which leads to improved performance. An RNN can also be used to predict the duration of activities. For this, we quantize the temporal extent of a trace in minutes and, for each minute, encode the current activity in the input. For example, if activity A occurs for 3 minutes, the input consists of the sequence AAA. Only the BPI 2012 W dataset includes both 6.3. Predicting Duration of Activities 20 Dataset Predictand only Predictor & Predictand Training Precision Validation Precision Training Precision Validation Precision Mean SD Mean SD Mean SD Mean SD BPI2013.Incidents .832 .034 .761 .049 .731 .010 .595 .061 BPI2013.Problems .776 .081 .616 .117 .764 .034 .517 .043 BPI2012 (completion) .833 .004 .811 .023 .622 .005 .594 .010 BPI2012 (all events) .880 .007 .866 .008 .685 .007 .663 .015 BPI2012.W (completion) .745 .013 .693 .019 .313 .012 .208 .023 BPI2012.W (all events) .865 .012 .845 .012 .609 .018 .559 .018 BPI2012.A .846 .006 .826 .007 .758 .010 .709 .008 BPI2012.O .870 .015 .833 .015 .703 .026 .590 .020