Decomposed and parallel process discovery: A framework and application

Yan, Zhiqiang; Sun, Bo; Chen, Yu; Wen, Lijie; Hu, Lei; Wang, Jianmin; Ming-ji, Yang; Wang, Lu

doi:10.1016/j.future.2019.03.048

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…There are also graph-based α algorithm, such as Graph-based Parallel [15] and Graph-based Invisible Task [22]. Other algorithms are developed, such as Inductive Miner [9] and RPST [10]. Table 1 describes the existing algorithms which can discover types of relations depicted by the columns.…”

Section: Existing Methods Of Process Discoverymentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Existing Methods Of Process Discoverymentioning

confidence: 99%

“…The process discovery algorithms have been developed rapidly to discover several types of relations [8][9][10][11][12][13]; however, seldom algorithms concern with invisible tasks in non-free choice. α $ [14] is the development of α algorithm that discovers a new issue, i.e., invisible tasks in non-free choice.…”

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confidence: 99%

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

et al. 2021

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“…The algorithms of process discovery have been developed rapidly to discover several types of relations [8][9][10][11][12][13]. Table 1 describes the existing algorithms which can discover types of relations depicted by the columns.…”

Section: Introductionmentioning

confidence: 99%

Improving Efficiency for Discovering Business Processes Containing Invisible tasks in Non-free Choice

Sarno

Sungkono

Taufiqulsa’di

et al. 2020

Preprint

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Process discovery helps companies to automatically discover their existing business processes based on the huge, stored event log. The algorithms of process discovery 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 α $ method, is a type of relation that combines the non-free choice and the invisible task. 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 α $ check the combination of invisible task within non-free choice. The checking processes is time consuming, and results in high computing times of 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 relations of the graphs. The analysis of the graph relations by rules of GIT is more efficient than the iterative process of checking combined activities by α $. This research measures the time efficiency of storing the event log and discovering a process model to evaluate GIT algorithm. Storing a streaming event log in a graph-database has the lowest computing time than storing in other databases, i.e., SQL and MongoDB. Discovering a process model by GIT algorithm has less time complexity than that by α $, wherein GIT obtains O(n3) and α $ obtains O(n4) . In terms of computing time, GIT algorithm is 0.89 faster on batch event log and 0.85 seconds faster on streaming event log than α $. Those results of the evaluation show a significant improvement of GIT method in term of time efficiency.

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“…image classification [27]- [31]. Yan [19] proposed a graph algorithm for process mining; however, this algorithm is not discussed in this paper because the graph is used for decomposing the log and that paper uses rules of the heuristic miner for depicting a process model. This research analyzes all of the graph-based algorithms.…”

mentioning

confidence: 99%

A survey of graph-based algorithms for discovering business processes

Sarno

Sungkono

2019

Int. J. Adv. Intell. Informatics

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Algorithms of process discovery help analysts to understand business processes and problems in a system by creating a process model based on a log of the system. There are existing algorithms of process discovery, namely graph-based. Of all algorithms, there are algorithms that process graph-database to depict a process model. Those algorithms claimed that those have less time complexity because of the graph-database ability to store relationships. This research analyses graph-based algorithms by measuring the time complexity and performance metrics and comparing them with a widely used algorithm, i.e. Alpha Miner and its expansion. Other than that, this research also gives outline explanations about graph-based algorithms and their focus issues. Based on the evaluations, the graph-based algorithm has high performance and less time complexity than Alpha Miner algorithm.

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Decomposed and parallel process discovery: A framework and application

Cited by 5 publications

References 15 publications

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

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

Improving Efficiency for Discovering Business Processes Containing Invisible tasks in Non-free Choice

A survey of graph-based algorithms for discovering business processes

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