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

Abstract

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 $$\alpha ^{\$ }$$ α $ method, is a type of relationship that combines the non-free choice and the invisible task. $$\alpha ^{\$ }$$ α $ 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 $$\alpha ^{\$ }$$ α $ check the combination of invisible task within non-free choice. The checking processes are time-consuming and result in high computing times of $$\alpha ^{\$ }$$ α $ . 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 $$\alpha ^{\$ }$$ α $ . 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 $$\alpha ^{\$ }$$ α $ , wherein GIT obtains $$O(n^{3} )$$ O ( n 3 ) and $$\alpha ^{\$ }$$ α $ obtains $$O(n^{4} )$$ O ( n 4 ) . Those results of the evaluation show a significant improvement of GIT method in term of time efficiency.

Graph-Based Token Replay for Online Conformance Checking

“…This study proposes a Graphbased invisible task (GIT) to perform a process model discovery that contains an invisible task. Next, Sarno et al [7] designed the event log storage from ERP directly to the graph database so that there is no need to convert the event log format to perform process discovery. The GIT algorithm is superior in time complexity and computational time compared to other discovery methods [7].…”

“…In line with Graph-based process discovery [7], the graph database is used to propose online conformance checking that avoids memory constraints and is robust against unknown future behavior.…”

“…1. If an event log is available in a (semi) structured format that can be imported into the graph database (or it could be that the event log is already available natively in the graph database), then a graph-based process model discovery is made [7], [20]- [22]. The results obtained are still in the directly followed graph (DFG) representation, so the next step is to convert to Petri net using algorithm 1.…”

“…The graph database was chosen because of its ability to store graph data natively. Previous study by Sarno et al [7] had succeeded in integrating an ERP system with a graph database to store event logs directly and applying a graphbased process discovery model technique. Several developments and applications also show that the graphbased methods can provide high fitness and precision scores [8], [9].…”

Perbandingan Model Proses Algoritma Alpha dan Alpha++ Pada Aplikasi E-commerce

“…Dalam penerapan process mining [10][11] bahwa event log perlu beberapa pre-processing sebelum dapat dianalis dengan process mining tool. Pada proses discovery algoritma telah dilakukan pengembangan untuk menemukan beberapa jenis relation [11].…”