# Explainability of Predictive Process Monitoring Results: Can You See My Data Issues?
## 5 Results
### Data- and Preprocessing-Related Observations  
In the data analysis experiments we performed _before preprocessing_ the event logs, we made several observations. Moreover, in the data analysis experiments we performed _after preprocessing_ the event logs, we could observe the effects of preprocessing configurations. Studying the data characteristics before and after preprocessing the logs revealed a number of interesting observations, which are described in detail below. Figure 4: Experiments summary. **Data characteristics after preprocessing:**  
_Observation 1:_ _Several features that have a constant value before encoding show non-constant values after preprocessing depending on the applied encoding technique._  
**Example 1**. : _in the_ BPIC2017_Refused _event log, the feature \"EventOrigin\" has one value labelled \"other\" before preprocessing, and has 20 such values after applying frequency aggregation encoding. Moreover, certain features remain constant after encoding, e.g., \"case:ApplicationType\" in the same event log after its encoding based on frequency aggregations._  
_Observation 2:_ _Certain features show a high number of zeros (i.e., have sparse vectors) before and after encoding._  
**Example 2**. : _in Traffic_fines, the \"points\" feature has a sparse vector of about 97% zeros before encoding. This percentage is the same for both encoded versions of the same event log. However, the percentage of zeros increases in almost all event logs after preprocessing, as a result of applying either one-hot encoding in index encoding or counting the frequencies of categorical levels in aggregation encoding. The percentage of zeros reaches 99% in event logs encoded with index encoding, e.g., Traffic_fines._  
Moreover, after applying aggregation encoding techniques, certain event logs show a relatively low percentage of zeros, e.g., _Sepsis2_, which has about 33% of zeros, whereas other event logs have a high percentage of zeros, from 44 to 99%, e.g., _BPIC_2017_. **This observation can be justified by two factors**:  
* _The effect of the bucketing technique._ Aggregation encoding is combined with single bucketing, which buckets all prefixes in the same group. Having many prefixes of the same process instance is more likely to reduce the effect of feature value imbalances. Such imbalances can happen due to the presence of prefixes generated from process instances with longer trace lengths. Moreover, index encoding is combined with prefix bucketing, which reduces the number of process instances fed into each encoding technique. As a result, combining index encoding with prefix-based bucketing has the potential to magnify imbalances in feature values. * _The difference in \"zero' indication in both encoding techniques._ In aggregation encoding, a zero means that the feature did not have any value in the encoded event. In turn, in index encoding, a zero indicates whether the feature has a value in the process instance. Note that after aggregation encoding, a process instance might be represented along many rows, whereas after index encoding, it is represented by exactly one row. As a result, a high number of zeros does not guarantee the absence of a feature value after preprocessing certain events using aggregation encoding. A high number of zeros after index encoding, however, denotes the absence of a value. In summary, a feature might be considered for a predictive model even though it has a large percentage of zeros. _Observation 3:_ _Categorical levels extracted from certain categorical attributes dominate in prefix-indexed versions of certain event logs. This dominance increases with the length of the prefixes._  
This observation holds in the dominance of categorical levels of the \"credit_score\" feature in the prefix-indexed version of _BPIC2017_Refused_ logs (cf. Figure 5).