# Event Log Preprocessing for Process Mining: A Review
## 3 Research Methodology
### C2. Tools  
What tools are available for the event logs preprocessing task? Tools for event log preprocessing are generally included as part of some tools for process mining. However, many commercial software tools for process mining (Perceptive Process Mining by Lexmark [82], Interstage Business Process Manager Analytics by Fujitsu Ltd. [83], Minit by Gradient ECM [84], myInvenio by Cognitive Technology [85], etc.) do not support event logs preprocessing tasks that help improving the quality of event logs. There are particular tools, applications, or frameworks developed for specific  
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preprocessing tasks of event logs [26; 36; 37; 48; 52; 53; 72]. Most of these tools are limited to a single process modeling language and use some type of data deployment or transformation. Moreover, there are specialized tools such as ProM [16], Apromore [86], Celonis [87], and RapidProm [88] that include different filters, routines, and algorithms for preprocessing the event log to support process mining tasks. According to [PERSON]. [8], there are three categories of process mining tools that contain event log preprocessing. Type-1 process mining tools are mainly built for answering ad-hoc questions about event log preprocessing. An example of this tool type is Disco [89], which allows the user to interactively filter the data and project that data immediately on a newly learned process model. In Type-2 process mining tools, the analytic workflow is made explicit; that is, the user can visualize and decide what elements to isolate or eliminate from the event log. An example of this tool type is RapidProM. Finally, tools of Type-3 are tailored towards answering predefined questions repeatedly in a known setting. These tools are typically used to create \"process dashboards\" that provide standard views of process models. For example, the tool called Celonis Process Mining supports the creation of such process-centric dashboards. Next, we describe some tools that include preprocessing or event log repair strategies as part of their functioning. Among the criteria considered to select these tools are their popularity in the process mining area (as they are reported in several papers) and the inclusion of preprocessing techniques. The ProM framework [16] provides different event log filters (Filter event log based on choice, Filter events based on attribute value, filter log using simple heuristics, filter in high-frequency trace, among others) for cleaning event logs. These filters are especially useful when handling real-life logs and they do not only allow for projecting data in the log, but also for adding data to the log, removing process instances (cases), and removing and modifying events. There are several other filter plug-ins in ProM for the removal or repairing of activities, attributes, and events (Remove activities that never have utility, remove all attributes with value-empty, remove events without timestamps, refine labels globally, etc.). ProM is the most popular process mining tool that mostly has preprocessing techniques, since many of the research proposals are available from ProM. However, most of the available preprocessing techniques are focused on event filtering and trace clustering. ProM handles multiple formats and multiple languages, e.g., Petri nets, BPMN, EPCs, social networks, etc. Through the import of plug-ins, a wide variety of models can be loaded ranging from a Petri net to LTL formulas. The ProM framework allows for interaction between a large number of plug-ins, i.e., implementations of algorithms and formal methods for analysis of business process, process mining, social network analysis, organizational mining, clustering, decision mining, prediction, and recommendation. Apromore [86] is an open-source platform for advanced models of business processes. It allows applying a variety of filtering techniques to slice and dice an event log in different ways. There are two main filter types supported by Apromore: case filter and event filter.