# Integrating ISA and Part-of Domain Knowledge into Process Model Discovery
## 3 Preliminaries
### SIM: Generalities  
The work proposed in this paper grounds on SIM, a novel miner supporting experts in balancing overfitting and underfitting in the process discovery task [2]. SIM supports process analysis through query and retrieval facilities, while semantic \"merge\" allows for building increasingly more generalized models. In addition, SIM provides fully automatic process discovery to be used when expert(s) knowledge is not available. A detailed description of SIM can be found in [3].  
SIM consists of four main modules: (1) a _mining module_ to build an initial process model (_log tree_); (2) a _trace and path retrieval module_ to search for patterns in the log tree or in any generalized models, using a flexible and high-level query language; (3) a general \"_merge\" module_ supporting the generalization of process models by merging patterns in the current model; (4) a _versioning module_ supporting the navigation through the history of the model evolution interaction by maintaining a versioning tree.  
A work session in SIM starts running an algorithm (called _Build-Tree_), exploiting the traces in the log to mine a process model, called _log tree_, having both precision and replay fitness = 1. This means that each path in the log tree corresponds to at least one trace and that each trace can be successfully replayed in the log tree [4] (there are two main justifications for such a choice: (1) certain domains (e.g., medical applications) require a maximal precision or replay fitness; (2), in such a way, generalizations are driven \"step-by-step\" by experts (see [3] for a more detailed analysis of such motivations)). These features make the log tree a potentially overfitted model; therefore, the experts can apply _merge operations_ to obtain progressively more generalized process models. Given a pattern of occurrences of a pattern of activities, described with our query language, the retrieval facility highlights all the occurrences of the pattern in the current model. Then, merge operations generate a new model by \"unifying\" these occurrences in a single pattern and updating the arcs accordingly. Notably, SIM offers different merge options, as well as the possibility of performing a selective merge on a subset of the occurrences. Each process model can then be evaluated by experts both quantitatively (i.e., when a new model is generated, SIM evaluates its precision, replay fitness, and generalization) and qualitatively. Experts can evaluate each process model quantitatively, through measures such as precision, replay fitness, and generalization, and qualitatively with the help of the query and pattern retrieval facility. Furthermore, the versioning mechanism provided in SIM maintains the history of the obtained process models. Experts can navigate this history to select a particular model, obtain new versions from it, or backtrack to a previous one. Finally, the work session ends when the expert \"approves\" one of these models.  
In the following, we briefly consider two specific aspects of SIM to understand the approach we propose in this paper: SIM's formalism to represent the process model and SIM's query language and query answering. Furthermore, in Section 6, we present extensions of the SIM's versioning system to support knowledge-based abstraction versioning.