Graph-Based Process Model Discovery for Mining Invisible Non-Prime Tasks in Hybrid Choice-Parallel Relationships

Process discovery is a study to form a process model based on an event log. The output of process discovery is a process model that contains linked activities based on performed business processes. There are several kinds of relationships and additional invisible tasks in forming a process model. The current invisible tasks, called invisible prime tasks, are divided into three conditions, i.e. skip, redo and switch. However, the need for invisible task is not only in those conditions but also in a situation, which is hybrid choice-parallel relationships. Hybrid choice-parallel relationships occur when there are choice relationships, namely XOR, pile up with parallel relationships for all branching activities (AND) or parallel relationships for several branching activities (OR). This research proposes graph-based Invisible-Task Prime and Non-Prime (ITPNP) algorithm that mines invisible prime tasks and invisible non-prime task in the cased of hybrid parallel relationships. Graph-based ITPNP algorithm creates rules for detecting invisible task needs based on relationships occurring in the graph model. The evaluation is implemented with seven processes and four comparison methods, i.e. Alpha#, Alpha Mining, Fodina, and Graph-based Invisible Task. Among all algorithms, graph-based ITPNP obtains one of fitness for all event logs and one of precision in four event logs. On the other hand, Fodina algorithm has 0.6 precision which is lower than graph-based ITPNP in the event logs requiring invisible non-prime tasks of hybrid XOR-OR. Besides, Graph-based Invisible Task has low fitness, i.e. 0.67 in Hazardous Waste Handling processes that depict choice relationships with additional invisible tasks for hybrid XORAND relationships.