Improving efficiency for discovering business processes containing invisible tasks in non-free choice

Process discovery helps companies automatically discover their existing business processes based on the vast, stored event log. The process discovery algorithms have been developed rapidly to discover several types of relations, i.e., choice relations, non-free choice relations with invisible tasks. Invisible tasks in non-free choice, introduced by alpha($) method, is a type of relationship that combines the non-free choice and the invisible task. alpha($) proposed rules of ordering relations of two activities for determining invisible tasks in non-free choice. The event log records sequences of activities, so the rules of alpha($) check the combination of invisible task within non-free choice. The checking processes are time-consuming and result in high computing times of alpha($). This research proposes Graph-based Invisible Task (GIT) method to discover efficiently invisible tasks in non-free choice. GIT method develops sequences of business activities as graphs and determines rules to discover invisible tasks in non-free choice based on relationships of the graphs. The analysis of the graph relationships by rules of GIT is more efficient than the iterative process of checking combined activities by alpha($). This research measures the time efficiency of storing the event log and discovering a process model to evaluate GIT algorithm. Graph database gains highest storing computing time of batch event logs; however, this database obtains low storing computing time of streaming event logs. Furthermore, based on an event log with 99 traces, GIT algorithm discovers a process model 42 times faster than alpha(++) and 43 times faster than alpha($). GIT algorithm can also handle 981 traces, while alpha(++) and alpha($) has maximum traces at 99 traces. Discovering a process model by GIT algorithm has less time complexity than that by alpha($), wherein GIT obtains O(n(3)) and alpha($) obtains O(n(4)). Those results of the evaluation show a significant improvement of GIT method in term of time efficiency.