Business Process Workflow Mining Using Machine Learning Techniques for the Rail Transport Industry

Bandis, Eleftherios; Petridis, Miltos; Kapetanakis, Stelios

doi:10.1007/978-3-030-04191-5_37

“…This information is available at prediction time and might be important for time predictions if many traces are using one resource simultaneously. Bandis et al (2018) drew upon domain knowledge to map the timestamp to binary variables signalling peak hours. Theis and Darabi (2019) added a complete preprocessing step and firstly mined a petri net from the event log.…”

Section: Data Preprocessing and Feature Engineeringmentioning

confidence: 99%

“…the coefficient of determination. The regression results of Bandis et al (2018); Wahid et al (2019) are not shown, since their evaluation was done on individual datasets with time scales, several magnitudes smaller.…”

Section: Prediction Targetmentioning

confidence: 99%

A systematic literature review on state-of-the-art deep learning methods for process prediction

Neu

¹

,

Lahann

²

,

Fettke

³

2021

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Process mining enables the reconstruction and evaluation of business processes based on digital traces in IT systems. An increasingly important technique in this context is process prediction. Given a sequence of events of an ongoing trace, process prediction allows forecasting upcoming events or performance measurements. In recent years, multiple process prediction approaches have been proposed, applying different data processing schemes and prediction algorithms. This study focuses on deep learning algorithms since they seem to outperform their machine learning alternatives consistently. Whilst having a common learning algorithm, they use different data preprocessing techniques, implement a variety of network topologies and focus on various goals such as outcome prediction, time prediction or control-flow prediction. Additionally, the set of log-data, evaluation metrics and baselines used by the authors diverge, making the results hard to compare. This paper attempts to synthesise the advantages and disadvantages of the procedural decisions in these approaches by conducting a systematic literature review.

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“…the coefficient of determination. The regression results of Bandis et al (2018) & Wahid et al (2019 are not shown, since their evaluation was done on individual datasets with time scales, several magnitudes smaller. After the individual results have been presented, the next section will synthesise and discuss the findings of the studies.…”

Section: Prediction Targetmentioning

confidence: 99%

A systematic literature review on state-of-the-art deep learning methods for process prediction

Neu,

Lahann,

Fettke

2021

Preprint

0

View full text Add to dashboard Cite

Process mining enables the reconstruction and evaluation of business processes based on digital traces in IT systems. An increasingly important technique in this context is process prediction. Given a sequence of events of an ongoing trace, process prediction allows forecasting upcoming events or performance measurements. In recent years, multiple process prediction approaches have been proposed, applying different data processing schemes and prediction algorithms. This study focuses on deep learning algorithms since they seem to outperform their machine learning alternatives consistently. Whilst having a common learning algorithm, they use different data preprocessing techniques, implement a variety of network topologies and focus on various goals such as outcome prediction, time prediction or control-flow prediction. Additionally, the set of log-data, evaluation metrics and baselines used by the authors diverge, making the results hard to compare. This paper attempts to synthesise the advantages and disadvantages of the procedural decisions in these approaches by conducting a systematic literature review.

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Defining Data Quality Issues in Process Mining with IoT Data

Bertrand

¹

,

Belle

²

,

Weerdt

³

et al. 2023

Lecture Notes in Business Information Processing

4

0

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IoT devices supporting business processes (BPs) in sectors like manufacturing, logistics or healthcare collect data on the execution of the processes. In the last years, there has been a growing awareness of the opportunity to use the data these devices generate for process mining (PM) by deriving an event log from a sensor log via event abstraction techniques. However, IoT data are often affected by data quality issues (e.g., noise, outliers) which, if not addressed at the preprocessing stage, will be amplified by event abstraction and result in quality issues in the event log (e.g., incorrect events), greatly hampering PM results. In this paper, we review the literature on PM with IoT data to find the most frequent data quality issues mentioned in the literature. Based on this, we then derive six patterns of poor sensor data quality that cause event log quality issues and propose solutions to avoid or solve them.

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Business Process Workflow Mining Using Machine Learning Techniques for the Rail Transport Industry

Cited by 3 publications

References 15 publications

A systematic literature review on state-of-the-art deep learning methods for process prediction

A systematic literature review on state-of-the-art deep learning methods for process prediction

A systematic literature review on state-of-the-art deep learning methods for process prediction

Defining Data Quality Issues in Process Mining with IoT Data

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