# Generating Block-Structured Parallel Process Models by Demonstration
###### Abstract  
Programming by demonstration (PBD) is a technique which allows end users to create, modify, accommodate, and expand programs by demonstrating what the program is supposed to do. Although the ideal of common-purpose programming by demonstration or by examples has been rejected as practically unrealistic, this approach has found its application and shown potentials when limited to specific narrow domains and ranges of applications. In this paper, the original method of applying the principles of programming by demonstration in the area of process mining (PM) to interactive construction of block-structured parallel business processes models is presented. A technique and tool that enable interactive process mining and incremental discovery of process models have been described in  
interactive specification of behavior by demonstration is not new, it exists in papers of many PBE and PBD paradigm researchers, and is precisely described by [PERSON] (1965), where the term \"intelligent interface agent\" implies a system (machine) which communicates with the user directly through user interface, which has the ability to reason and conclude and uses procedures to perform tasks that are demonstrated by a user. However, this paper shows how this same paradigm can be applied to process mining [PERSON] (1965); [PERSON] (1965) and inference of a specific category of business process models by a system through interactive demonstration. Many information systems have the possibility to record their execution and, in this way, generate a trace of events describing the real system behavior. Process mining techniques are based on the assumption that there is a strong relationship between process models and the \"reality\" recorded in the event log in the form of traces [PERSON] (1965). From behavioral sample records in the event log, the \(\alpha\)-algorithm [PERSON] (1965) automatically generates a process model that belongs to a subclass of Petri nets, known as workflow (WF) nets [PERSON] (1965). The property of completeness of the log required by the original \(\alpha\)-algorithm often implies the necessity of having a large number of traces in the log based on which the \"representative\" model for the behavior seen in the log has to be constructed. Therefore, our challenge was to find logs with potentially much lower number of traces, which may not be complete, but are sufficiently valid so that, using the appropriate algorithm based on the evidences recorded in such logs, a \"representative\" model can be obtained. To achieve this, we have partially modified the technique of process model discovering and also the \(\alpha\)-algorithm itself, the modified version of which we named the \(\alpha^{\,\mid\,\mid}\) -algorithm [PERSON] (1965); [PERSON] (1965). The graphical user interface that we created is a tool that visually shows steps of the \(\alpha^{\,\mid\,\mid}\) -algorithm. Such a tool could serve as a learning tool and playground for those who want to learn more about how the more general \(\alpha\)-algorithm that is based on the same principles as its modified version \(\alpha^{\,\mid\,\mid}\) functions. In existing process mining techniques used to automatically discover process models from event logs, the user has very little influence. On the other hand, the traditional process modeling tools which are completely user-driven do not use historical evidence from the event logs during process modeling. Our idea was to find a way to engage the user in discovering process models by taking part in the creation of the event log, so that the user can make certain decisions taking into consideration the contents of the event log. Moreover, we wanted to explore possibilities of incremental discovering of process models, where each incremental process model, the candidate model, would reflect the event log demonstrated up to the point. The model created in that way would be a real reflection of the process realization up to that moment and would result in total conformity of the model within the event log.