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 α⁺⁺ and 43 times faster than α^$. GIT algorithm can also handle 981 traces, while α⁺⁺ and α^$ 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.

流程发现可帮助公司根据大量存储的事件日志自动发现其现有业务流程。过程发现算法已经迅速发展到发现几种类型的关系，即选择关系、具有不可见任务的非自由选择关系。非自由选择中的隐形任务由\(\alpha ^{\$ }\)方法引入，是一种结合了非自由选择和隐形任务的关系。\(\alpha ^{\$ }\)提出了两个活动的排序关系规则，用于确定非自由选择中的隐形任务。事件日志记录了活动的顺序，所以\(\alpha ^{\$ }\)检查组合内的隐形任务，非自由选择。检查过程非常耗时，并且会导致\(\alpha ^{\$ }\) 的高计算时间。本研究提出了基于图的隐形任务（GIT）方法来有效地发现非自由选择中的隐形任务。GIT 方法将业务活动序列开发为图形，并根据图形的关系确定规则以在非自由选择中发现不可见的任务。通过GIT的规则分析图关系比通过\(\alpha ^{\$ }\)检查组合活动的迭代过程更有效. 本研究衡量存储事件日志和发现流程模型以评估 GIT 算法的时间效率。图数据库获得最高的批处理事件日志存储计算时间；然而，该数据库获得流媒体事件日志的低存储计算时间。此外，基于具有 99 条跟踪的事件日志，GIT 算法发现流程模型比 α ⁺⁺快 42 倍，比 α ^$快 43 倍。GIT 算法也可以处理 981 条痕迹，而 α ⁺⁺和 α ^{$ 的}最大痕迹在 99 条。通过 GIT 算法发现流程模型的时间复杂度低于通过\(\alpha ^{\$ }\)发现的时间复杂度，其中 GIT 获得\(O(n^{3} )\)和\(\alpha ^{\$ }\)获得\(O(n^{4} )\)。这些评估结果表明 GIT 方法在时间效率方面有显着改进。