Anomaly detection based on control-flow pattern of Parallel Business Processes

Hendra Darmawan; Riyanarto Sarno; Adhatus Solichah Ahmadiyah; Kelly Rossa Sungkono; Cahyaningtyas Sekar Wahyuni

doi:10.12928/TELKOMNIKA.v16i6.10568

Anomaly detection based on control-flow pattern of Parallel Business Processes

Hendra Darmawan^*, Riyanarto Sarno, Adhatus Solichah Ahmadiyah, Kelly Rossa Sungkono, Cahyaningtyas Sekar Wahyuni

^*Corresponding author for this work

Institut Teknologi Sepuluh Nopember

Research output: Contribution to journal › Article › peer-review

11 Citations (Scopus)

Abstract

The purpose of this paper was to discover an anomalous-free business process model from event logs. The process discovery was conducted using a graph database, specifically using Neo4J tool involving trace clustering and data filtering processes. We also developed a control-flow pattern to address, AND relation between activities named parallel business process. The result showed that the proposed method improved the precision value of the generated business process model from 0.64 to 0.81 compared to the existing algorithm. The better outcome is constructed by applying trace clustering and data filtering to remove the anomaly on the event log as well as discovering parallel (AND) relation between activities.

Original language	English
Pages (from-to)	2809-2816
Number of pages	8
Journal	Telkomnika (Telecommunication Computing Electronics and Control)
Volume	16
Issue number	6
DOIs	https://doi.org/10.12928/TELKOMNIKA.v16i6.10568
Publication status	Published - 1 Dec 2018

Keywords

Anomaly data filtering
Control-flow pattern
Graph database
Process discovery

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10.12928/TELKOMNIKA.v16i6.10568

Cite this

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abstract = "The purpose of this paper was to discover an anomalous-free business process model from event logs. The process discovery was conducted using a graph database, specifically using Neo4J tool involving trace clustering and data filtering processes. We also developed a control-flow pattern to address, AND relation between activities named parallel business process. The result showed that the proposed method improved the precision value of the generated business process model from 0.64 to 0.81 compared to the existing algorithm. The better outcome is constructed by applying trace clustering and data filtering to remove the anomaly on the event log as well as discovering parallel (AND) relation between activities.",

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author = "Hendra Darmawan and Riyanarto Sarno and Ahmadiyah, {Adhatus Solichah} and Sungkono, {Kelly Rossa} and Wahyuni, {Cahyaningtyas Sekar}",

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AU - Darmawan, Hendra

AU - Sarno, Riyanarto

AU - Ahmadiyah, Adhatus Solichah

AU - Sungkono, Kelly Rossa

AU - Wahyuni, Cahyaningtyas Sekar

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AB - The purpose of this paper was to discover an anomalous-free business process model from event logs. The process discovery was conducted using a graph database, specifically using Neo4J tool involving trace clustering and data filtering processes. We also developed a control-flow pattern to address, AND relation between activities named parallel business process. The result showed that the proposed method improved the precision value of the generated business process model from 0.64 to 0.81 compared to the existing algorithm. The better outcome is constructed by applying trace clustering and data filtering to remove the anomaly on the event log as well as discovering parallel (AND) relation between activities.

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KW - Process discovery

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Anomaly detection based on control-flow pattern of Parallel Business Processes

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Keywords

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