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Anomaly Detection based on Control-flow Pattern of Parallel Business Processes

Year: 2018

Type: article

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.

Source: TELKOMNIKA (Telecommunication Computing Electronics and Control)

DOI: 10.12928/telkomnika.v16i6.10568

Authors: Hendra Darmawan, Riyanarto Sarno, Adhatus Solichah Ahmadiyah, Kelly Rossa Sungkono, Cahyaningtyas Sekar Wahyuni

Institution: Institut Teknologi Sepuluh Nopember

Language: English

Topic: Business Process Modeling and Analysis

Subfield: Management Information Systems

Field: Business, Management and Accounting

Domain: Social Sciences

Open Access status: diamond

OpenAlex ID: W2896979080

FWCI: 4.152

Cites:

Cited by: 15

Related to: 10