The connection between process complexity of event sequences and models discovered by process mining

Augusto, Adriano; Mendling, Jan; Vidgof, Maxim; Wurm, Bastian

doi:10.1016/j.ins.2022.03.072

Cited by 33 publications

(8 citation statements)

References 26 publications

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“…To avoid concerns regarding synthetic data representativeness, we further assess the distribution of log behavior compared to real event logs. For that, we extracted all metafeatures (listed in Table 1) of the synthetic event logs plus three real business processes (Business Process Intelligence Challenges (BPIC) 2012 4 , BPIC 2013 and helpdesk 5 ).…”

Section: Methodsmentioning

confidence: 99%

“…For that, we retrieved the complexities of all synthetic event logs and the same three real processes. The measure chosen was the normalized variant entropy, recently proposed by Augusto et al [4], and is based on graph entropy. According to the authors, graph-based entropy is particularly suited for event logs as it captures size, variation and distance in an integral way.…”

Section: Methodsmentioning

confidence: 99%

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Matching business process behavior with encoding techniques via meta-learning: An anomaly detection study

Tavares

Barbon

2023

COMSIS J

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Recording anomalous traces in business processes diminishes an event log?s quality. The abnormalities may represent bad execution, security issues, or deviant behavior. Focusing on mitigating this phenomenon, organizations spend efforts to detect anomalous traces in their business processes to save resources and improve process execution. However, in many real-world environments, reference models are unavailable, requiring expert assistance and increasing costs. The con15 siderable number of techniques and reduced availability of experts pose an additional challenge for particular scenarios. In this work, we combine the representational power of encoding with a Meta-learning strategy to enhance the detection of anomalous traces in event logs towards fitting the best discriminative capability be tween common and irregular traces. Our approach creates an event log profile and recommends the most suitable encoding technique to increase the anomaly detetion performance. We used eight encoding techniques from different families, 80 log descriptors, 168 event logs, and six anomaly types for experiments. Results indicate that event log characteristics influence the representational capability of encodings. Moreover, we investigate the process behavior?s influence for choosing the suitable encoding technique, demonstrating that traditional process mining analysis can be leveraged when matched with intelligent decision support approaches.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Matching business process behavior with encoding techniques via meta-learning: An anomaly detection study

Tavares

Barbon

2023

COMSIS J

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“…Similar to picking and packing, all change eventsincluding quantity changes -are tracked in a change audit log (headers: CDHDR; items: CDPOS). 4 Limitations. Finally, it is important to point out that the presented O2C process constitutes a radical oversimplification.…”

Section: Document Flowmentioning

confidence: 99%

“…Will the data size to be extracted be overwhelming? 4 Table names in the SAP ECC data model are typically four to six character abbreviations of the context or document they capture. Because of the geographical origin of SAP, namely Germany, the abbreviations often stem from German.…”

Section: Data Selection and Extractionmentioning

confidence: 99%

“…Depending on the complexity of the source data model and process to be discovered, up to 80% of a project timespan is used for data preprocessing and log creation, leaving 20% for the real process analytical work [9]. While reviewing the existing literature, we have seen a focus on use-cases [14,23,25], on general approaches to (and techniques for) process analytics [5] and strategies and frameworks for creating event logs for process mining [4,17]. Recently, also data quality is receiving more attention [3].…”

Section: Introductionmentioning

confidence: 99%

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A Practitioner’s View on Process Mining Adoption, Event Log Engineering and Data Challenges

Accorsi

Lebherz

2022

Lecture Notes in Business Information Processing

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Process mining is, today, an essential analytical instrument for data-driven process improvement and steering. While practical literature on how to derive value from process mining exists, less attention haas been paid to how it is being used in different industries, the effort involved in creating an event log and what are the best practices in doing so. Taking a practitioner’s view on process mining, we report on process mining adoption and illustrate the challenges of log contruction by means of the order to cash (i.e. sales) process in an SAP system. By doing so, we collect a set of best practices regarding the data selection, extraction, transformation and data model engineering, which proved themselves handy in large-scale process mining projects.

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Advancing Business Process Science via the Co-evolution of Substantive and Methodological Knowledge

Mendling

2022

Lecture Notes in Computer Science

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The connection between process complexity of event sequences and models discovered by process mining

Cited by 33 publications

References 26 publications

Matching business process behavior with encoding techniques via meta-learning: An anomaly detection study

Matching business process behavior with encoding techniques via meta-learning: An anomaly detection study

A Practitioner’s View on Process Mining Adoption, Event Log Engineering and Data Challenges

Advancing Business Process Science via the Co-evolution of Substantive and Methodological Knowledge

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