A multi-perspective approach for the analysis of complex business processes behavior

Guzzo, Antonella; Joaristi, Mikel; Rullo, Antonino; Serra, Edoardo

doi:10.1016/j.eswa.2021.114934

Cited by 13 publications

(8 citation statements)

References 23 publications

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“…This study developed a good process recommendation system using Long Short-Term Memory (LSTM) and the Hierarchical Hidden Markov Model (HHMM). Another meaningful application is the conformance checking method based on activity and trace embedding for real-time process monitoring, which was developed by Peeperkorn, Vanden Broucke, and De Weerdt [36]. Their study demonstrated significantly better performance than other experiments that had randomly generated and detected noise based on virtual event logs.…”

Section: Event Log Embedding Methodsmentioning

confidence: 99%

Automatic Conversion of Event Data to Event Logs Using CNN and Event Density Embedding

Sim

Sutrisnowati²,

Won³

et al. 2022

IEEE Access

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Section: Event Log Embedding Methodsmentioning

confidence: 99%

Automatic Conversion of Event Data to Event Logs Using CNN and Event Density Embedding

Sim

Sutrisnowati²,

Won³

et al. 2022

IEEE Access

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“…Recent trends focus on encoding control-flow information into feature spaces, representing parallelism or optionality of activities [71,72]. Emerging approaches include multiperspective views of traces that combine both data-flow and control-flow [73,74]. Additionally, inter-case encoding, capturing relationships between different cases, has been explored [75].…”

Section: E Data Encodingmentioning

confidence: 99%

Tuning Machine Learning to Address Process Mining Requirements

Ceravolo,

Junior,

Damiani

et al. 2024

IEEE Access

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Machine learning models are routinely integrated into process mining pipelines to carry out tasks like data transformation, noise reduction, anomaly detection, classification, and prediction. Often, the design of such models is based on some ad-hoc assumptions about the corresponding data distributions, which are not necessarily in accordance with the non-parametric distributions typically observed with process data. Moreover, the learning procedure they follow ignores the constraints concurrency imposes on process data. Data encoding is a key element to smooth the mismatch between these assumptions but its potential is poorly exploited. In this paper, we argue that a deeper understanding of the challenges associated with training machine learning models on process data is essential for establishing a robust integration of process mining and machine learning. Our analysis aims to lay the groundwork for a methodology that aligns machine learning with process mining requirements. We encourage further research in this direction to advance the field and effectively address these critical issues.

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“…Applications include trace clustering and process model comparison. Future research avenues include interpretability and incorporating additional data dimensions-Joaristi et al [15] utilized event logs for business process analysis. Existing encoding methods focus on control flow, leaving out other aspects.…”

Section: Related Workmentioning

confidence: 99%

PMiner: Process Mining using Deep Autoencoder for Anomaly Detection and Reconstruction of Business Processes

Chinnaiah,

Veerabhadram,

Aavula

et al. 2024

Int. j. electr. comput. eng. syst. (Online)

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We proposed a deep learning-based process mining framework known as PMiner for automatic detection of anomalies in business processes. Since there are thousands of business processes in real-time applications such as e-commerce, in the presence of concurrency, they are prone to exhibit anomalies. Such anomalies if not detected and rectified, cause severe damage to businesses in the long run. Our Artificial Intelligence (AI) enabled framework PMiner takes business process event longs as input and detects anomalies using a deep autoencoder. The framework exploits a deep autoencoder technique which is well-known for Its ability to discriminate anomalies. We proposed an algorithm known as Intelligent Business Process Anomaly Detector (IBPAD) to realize the framework. This algorithm learns from historical data and performs encoding and decoding procedures to detect business process anomalies automatically. Our empirical results using the BPI Challenge dataset, released by the IEEE Task Force on Process Mining, revealed that PMiner outperforms state-of-the-art methods in detecting business process anomalies. This framework helps businesses to identify process anomalies and rectify them in time to leverage business continuity prospects.

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A multi-perspective approach for the analysis of complex business processes behavior

Cited by 13 publications

References 23 publications

Automatic Conversion of Event Data to Event Logs Using CNN and Event Density Embedding

Automatic Conversion of Event Data to Event Logs Using CNN and Event Density Embedding

Tuning Machine Learning to Address Process Mining Requirements

PMiner: Process Mining using Deep Autoencoder for Anomaly Detection and Reconstruction of Business Processes

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