An Improved Method of Parallel Model Detection for Graph-Based Process Model Discovery

Waspada, Indra; Sarno, Riyanarto; Sungkono, Kelly Rossa

doi:10.22266/ijies2020.0430.13

Cited by 11 publications

(6 citation statements)

References 16 publications

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“…The Graph-based Invisible Task (GIT) approach was presented by Sarno et al [8], [14] for the purpose of identifying invisible tasks using a graph database. Additionally, Waspada et al [9] created a Graph-based Process Discovery (GPD) to improve GIT so that it can operate more broadly by focusing on the frequency on the edge and taking concurrency and frequency into account in the detection of exclusive OR (XOR), parallel (AND), and inclusive OR (OR) patterns. In the sections below, we'll go over the GPD techniques employed in [9].…”

Section: A Process Discovery Methodsmentioning

confidence: 99%

“…Then proceed with the second stage to determine the join using the refined process structure tree (RPST) algorithm. The GPD algorithm [8], [9] utilizes the cypher algorithm on the Neo4j graph database to detect split and join parts and uses concurrent path frequency values to distinguish between AND and OR. However, these state-of-the-art process discovery algorithms have not been able to detect process flows that represent incomplete concurrency, which will reduce the quality of the resulting model.…”

Section: Introductionmentioning

confidence: 99%

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Extending Graph-Based Process Discovery for Hierarchical Block Detection and Incomplete Concurrent Relationship Handling

Waspada,

Sarno,

Astuti

et al. 2023

IEEE Access

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The flow of business process activities in an organization can run simply in the form of sequential or complex in the form of choice and parallel. In the discovery process, parallel conditions can be deduced from the concurrent flows between activities. Concurrent patterns can be identified by tracing pairs of sequences in opposite directions between two activities. Long parallel blocks allow incomplete concurrent relationship (ICR) phenomena when the observed data only provides a one-way sequence flow against conditions that should be concurrent. All state-of-the-art algorithms starting from Graph-based Process Discovery (GPD), Inductive Miner (IM), and Split Miner (SM), detect ICR as a sequence so that the quality of the resulting model decreases. This paper proposes the GPD++ algorithm for block detection of complex structures by combining algorithms from GPD and SM and handling ICR for each detected parallel block. The experiment compared IM, SM, and GPD++ using real-life data from the container loading and unloading process during import activities and some public data. The experimental results show that only GPD++ has successfully detected and corrected ICR to obtain the best fitness and precision values. In addition, GPD++ is also the only algorithm that can find hierarchies in the same type of complex structure, resulting in a block model with consistent split-join pairs.

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Section: A Process Discovery Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Extending Graph-Based Process Discovery for Hierarchical Block Detection and Incomplete Concurrent Relationship Handling

Waspada,

Sarno,

Astuti

et al. 2023

IEEE Access

Self Cite

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“…HMM-Parallel Tasks [20] and CHMM-Invisible Tasks [21] modifies rules of α in the form of Hidden Markov Models. 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].…”

Section: Existing Methods Of Process Discoverymentioning

confidence: 99%

“…This method is effective because a relationship can be detected from other relationships. For example, the graph-based algorithm discovers parallel relationships based on occurrences of sequence relationships [15]. An invisible task is detected based on parallel relationships.…”

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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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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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“…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.…”

Section: A Model Referencementioning

confidence: 99%

Graph-Based Token Replay for Online Conformance Checking

et al. 2022

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Conformance checking is able to detect deviations in business process execution. An online detection capability is required to anticipate and respond immediately to possible impacts. The state-of-theart online conformance checking is a prefix-alignment (PA) technique. However, this technique has an important weakness of maintaining all the state data of running processes in memory. In an online environment, the last event of a case is unknown, whereas a PA requires this information to free up memory space for other cases. Consequently, the PA does not meet the requirements of online conformance checking to process infinite data (event stream) without memory constraints. PA also has a conplex state space search computation especially for large and complex reference process model. In this paper, a Graph-Based Online Token Replay (GO-TR) method is proposed. This method takes benefit of Graph Database to adapts the Token-Based Replay (TBR) technique which has simple replay computation. We propose a Replay Image (RI) to store the case administration and developed a cypher based algorithm to simulate token replay on the RI to handle the event stream. Finally, we propose a cypher-based algorithm to identify and replay invisible path. The experiment result show that GO-TR has succeeded in adapting TBR and solved the problem of wrong-place tokens in TBR. GO-TR outperform the replay throughput for relatively low amount of data against state of the art online conformance checking. In terms of memory usage, GO-TR shows its superiority over state of the art because it is safe against memory limitations problems.

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An Improved Method of Parallel Model Detection for Graph-Based Process Model Discovery

Cited by 11 publications

References 16 publications

Extending Graph-Based Process Discovery for Hierarchical Block Detection and Incomplete Concurrent Relationship Handling

Extending Graph-Based Process Discovery for Hierarchical Block Detection and Incomplete Concurrent Relationship Handling

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

Graph-Based Token Replay for Online Conformance Checking

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