Neural Approach to the Discovery Problem in Process Mining

Shunin, Timofey; Zubkova, N. S.; Shershakov, Sergey

doi:10.1007/978-3-030-11027-7_25

Cited by 5 publications

(3 citation statements)

References 12 publications

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“…Here, various techniques for TS construction can be used, for instance, prefix tree synthesis [3], frequency based reduction [16], neural approach [17] etc. However, the TS synthesis can be bypassed and a Petri net can be generated directly from the event log (II).…”

Section: Frameworkmentioning

confidence: 99%

Method for Building UML Activity Diagrams from Event Logs

Zubkova¹,

Shershakov

2019

Proceedings of ISP RAS

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UML Activity Diagrams are widely used models for representing software processes. Models built from event logs, recorded by information systems, can provide valuable insights into real flows in processes and suggest ways of improving those systems. This paper proposes a novel method for mining UML Activity Diagrams from event logs. The method is based on a framework that consists of three nested stages involving a set of model transformations. The initial model is inferred from an event log using one of the existing mining algorithms. Then the model, if necessary, is transformed into an intermediate form and, finally, converted into the target UML Activity Diagram by the newly proposed algorithm. The transforming algorithms, except one used at the last stage, are parameters of the framework and can be adjusted based on needed or available models. The paper provides examples of the approach application on real life event logs.

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Section: Frameworkmentioning

confidence: 99%

Method for Building UML Activity Diagrams from Event Logs

Zubkova¹,

Shershakov

2019

Proceedings of ISP RAS

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“…Due to the sequential nature of business processes, RNNs were a good fit to approach the predictive monitoring problem [23], [24], [25]. Nowadays, deep learning has been widely applied to the predictive monitoring of business processes and, in general, in process mining tasks such as reconstructing missing events [26], anomaly detection [27], resource allocation [28] or process discovery [29].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Predictive Business Process Monitoring: Review and Benchmark

Rama-Maneiro¹,

Vidal²,

Lama³

2020

Preprint

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Predictive monitoring of business processes is concerned with the prediction of ongoing cases on a business process. Lately, the popularity of deep learning techniques has propitiated an ever-growing set of approaches focused on predictive monitoring based on these techniques. However, the high disparity of process logs and experimental setups used to evaluate these approaches makes it especially difficult to make a fair comparison. Furthermore, it also difficults the selection of the most suitable approach to solve a specific problem. In this paper, we provide both a systematic literature review of approaches that use deep learning to tackle the predictive monitoring tasks. In addition, we performed an exhaustive experimental evaluation of 10 different approaches over 12 publicly available process logs.

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“…These incremental methods derive relevant patterns recursively. Beside the standard induction methods, we can find some recent proposals for the application of neural networks in process mining [8], [9].…”

mentioning

confidence: 99%

ICCC 2023 Panel Discussion

2023

2023 24th International Carpathian Control Conference (ICCC)

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Neural Approach to the Discovery Problem in Process Mining

Cited by 5 publications

References 12 publications

Method for Building UML Activity Diagrams from Event Logs

Method for Building UML Activity Diagrams from Event Logs

Deep Learning for Predictive Business Process Monitoring: Review and Benchmark

ICCC 2023 Panel Discussion

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