# Integrating ISA and Part-of Domain Knowledge into Process Model Discovery
## 2 Related Work
Abstraction of non-automatically discovered process models could be considered in our future work. Regarding the interactivity in process model discovery, the literature has recently considered the possibility of exploiting expert's knowledge to this end [26]. The work in [27] proposed a hybrid method where domain knowledge was encoded in the form of precedence constraints. In [28], Bayesian statistics was relied upon to incorporate knowledge. The work in [29] focused on declarative process models and pruned constraints that, based on domain knowledge, can be inferred from other constraints. It is, however, worth noting that, in all of these contributions, domain knowledge must necessarily be expressed in the form of rules or constraints, and the user cannot control the discovery of the process, which, in the end, can be very complex. The approach in [30] is more incremental, since it allows the user to choose the traces to add to the existing process models. The mined process model is, thus, incrementally extended. The drawback is the complexity of choosing the correct traces when the noise is high. A very interesting contribution is described in [26], which introduces interactive process discovery (IPD). IPD exploits domain knowledge and the activity log in parallel, improving accuracy and understandability. To enable interactive discovery, IPD uses the so-called synthesis rules, which allows for expanding a minimal synthesized net (a type of Petri net) by adding one transition and/or one place selected by the user at a time. IPD indicates to the user whether the selected activity occurs before or after the other activities within the synthesized net. Alternatively, it highlights that the selected activity and the activities in the network never take place at the same time. IPD indicates to the user whether the selected activity occurs before or after the other activities within the synthesized net and suggests where to place it, based on the log. The user can ignore the suggestion. The paper in [31] discussed the suitability of IPD to healthcare--however, it is worth noting that, to the best of our knowledge, none of the current interactive approaches have been applied to real-life huge and noisy event logs, as is often the case in medical applications. Finally, the work in [32] discusses the possibilities of adopting interactive pattern recognition for analyzing the data provided by health sensors, in order to obtain new dynamic models for chronic conditions that consider patient behavior over time, instead of static values. Interactive pattern recognition is a formal framework based on process mining, which introduces the health expert to the learning process and allows her/him to correct the hypothesis model in each iteration to prevent unsatisfactory errors and to assemble a solution iteratively. It is, however, important to highlight that all of these interactive approaches do not specifically deal with semantic abstraction. As a final consideration, it is worth mentioning that, besides the already cited works in [31; 32], several additional specific applications of process mining to healthcare are being investigated. Indeed, healthcare presents particular challenges and issues [33], such as patient individuality, dynamic evolution, and hospital local resource constraints. Relevant contributions in this field are, e.g., [34; 35; 36; 37]. The survey in [37] showed how the medical process mining is primarily applied in the control flow discovery perspective,which is also our area of interest. Our work, which is general, but particularly well suited for medical applications (in fact, we will exemplify an approach referring to the medical domain), thus, is inserted in a very active research area.