# Event Log Preprocessing for Process Mining: A Review
## 3 Research Methodology
### C5. Related Tasks
#### 3.6.1 Event Abstraction  
The majority of available process mining techniques assume that event data are captured on the same level of granularity. However, information systems in the real world record events at different granularity levels [95]. In many cases, events recorded in one event log are presented in a fine-grained level, causing process techniques and particularly process discovery algorithms to produce incomprehensible process models or models not representative of the event log. In these cases, the event abstraction techniques transform the event log to a higher level of granularity, allowing to bridge the gap between an original low-level event log and a desired high-level perspective on the log, such that more comprehensible process models can be discovered.  
Some techniques proposed for event abstraction make use of supervised learning when annotations with high-level interpretations of the low-level events are available for a subset of the sequences (i.e., traces). These annotations provide guidance on how to label higher level events and guidance for the target level of abstraction. A general approach to supervised abstraction of events takes two inputs: (1) a set of annotated traces; that is, traces where the high-level event to which a low level event belongs (the label attribute of the low-level event) is known for all low-level events in the trace; and (2) a set of unannotated traces; that is, traces where the low level events are not mapped to high-level events.  
[PERSON] et al. [77] propose a method to abstract events in a XES event log that is too low-level, based on supervised learning and a condition random field learning step. A high-level interpretation of a low-level event log is achieved through a supervised learning model on the set of traces where high-level target labels are available, and applying the model to other low-level traces is possible to classify them. The recognition of high-level event labels is viewed as a sequence labeling task in which each event is classified as one of the higher-level events from a high-level event alphabet. That work proposes a sequence-focused metric to evaluate supervised event abstraction results that fits closely to the tasks of process discovery and conformance checking. Conditional random fields are trained from the annotated traces to create a probabilistic mapping from low-level events to high-level events. This mapping, once obtained, can be applied to the unannotated traces in order to estimate the corresponding high-level event for each low-level event.  
[PERSON] and [PERSON] [73] propose a process model abstraction technique to optimize the quality of the potential high level model and to consider the quality of the submodels generated where each sub-model is employed to show the details of its relevant high level activity in the high level model.  
There are some others methods explored within the process mining field that address the challenge of abstracting low-level events to higher level events [64; 65; 69; 73; 74]. Existing event abstraction methods rely on unsupervised learning techniques [76; 78] for clustering of low-level events into one high-level event. Current techniques require the user/process analyst to provide high-level event labels themselves based on domain knowledge, or generate long labels by concatenating the labels of all low-level events incorporated in the cluster. Many existing unsupervised event abstraction methods contain one or more parameters to control the degree in which events are clustered into higher level events.  
Finding the right level of abstraction that provides meaningful results is often a matter of trial-and-error.