A composite machine-learning-based framework for supporting low-level event logs to high-level business process model activities mappings enhanced by flexible BPMN model translation

Al-Ali, Hamda; Cuzzocrea, Alfredo; Damiani, Ernesto; Mizouni, Rabeb; Tello, Ghalia

doi:10.1007/s00500-019-04385-6

Cited by 5 publications

(2 citation statements)

References 39 publications

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“…The BPMN transforms a business process into a visual graph by using simple graphical notations, which simplifies business process modeling and visualizes complex modeling processes [24,25]. The standard BPMN models can be used to define process behavior because they contain decision logic in their control flow structure [26]. The WfMS based on the jBPM (java business process management) workflow engine aims at the typical phenomenon that it has a clear demand analysis process, that is, the modeling process of BPM; hence, we modeled the real processes according to the BPMN elements.…”

Section: Modeling and Analysis For Bpmnmentioning

confidence: 99%

A Software Testing Workflow Analysis Tool Based on the ADCV Method

Mao,

Han,

et al. 2023

Electronics

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Based on two progressive aspects of the modeling problems in business process management (BPM), (1) in order to address the increasing complexity of user requirements on workflows underlying various BPM application scenarios, a more verifiable fundamental modeling method must be invented; (2) to address the diversification of software testing processes, more formalized advanced modeling technology must also be applied based on the fundamental modeling method. Aiming to address these modeling problems, this paper first proposes an ADCV (acquisition, decomposition, combination, and verification) method that runs through the core management links of four types of business processes (mining, decomposition, recombination, and verification) and then describes the compositional structure of the ADCV method and the design of corresponding algorithms. Then, the software testing workflow is managed and monitored using the method, and the corresponding analysis tool is implemented based on Petri nets. At the same time, the tool is applied to the case processing of the software testing workflow. Specifically, the workflow models are established successively through ADCV during the process of business iteration. Then, the analysis tool developed with the ADCV method, the model–view–controller (MVC) design pattern, and Java Swing technology are applied to instances of the software testing workflow to realize the modeling and management of the testing processes. Thus, the analysis tool can guarantee the accuracy of the parameter estimations of related software reliability growth models (SRGMs) and ultimately improve the quality of software products.

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Section: Modeling and Analysis For Bpmnmentioning

confidence: 99%

A Software Testing Workflow Analysis Tool Based on the ADCV Method

Mao,

Han,

et al. 2023

Electronics

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“…Machine Learning (ML)-based methods for building prediction models have attracted abundant scientific attention and are extensively used in industrial engineering [1][2][3], design optimization of electromagnetic devices, and other areas [4,5]. The ML-based methods have been confirmed to be effective for solving real-world engineering problems [6][7][8]. Various supervised ML techniques (e.g., artificial neural network, support vector machine, classification and regression tree, linear (ridge) regression, and logistic regression) are typically used individually to construct single models and ensemble models [9,10].…”

Section: Introductionmentioning

confidence: 99%

Solving Regression Problems with Intelligent Machine Learner for Engineering Informatics

2021

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Machine learning techniques have been used to develop many regression models to make predictions based on experience and historical data. They might be used singly or in ensembles. Single models are either classification or regression models that use one technique, while ensemble models combine various single models. To construct or find the best model is very complex and time-consuming, so this study develops a new platform, called intelligent Machine Learner (iML), to automatically build popular models and identify the best one. The iML platform is benchmarked with WEKA by analyzing publicly available datasets. After that, four industrial experiments are conducted to evaluate the performance of iML. In all cases, the best models determined by iML are superior to prior studies in terms of accuracy and computation time. Thus, the iML is a powerful and efficient tool for solving regression problems in engineering informatics.

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Selected Aspects of Interactive Feature Extraction

Grzegorowski¹

2022

Lecture Notes in Computer Science

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A composite machine-learning-based framework for supporting low-level event logs to high-level business process model activities mappings enhanced by flexible BPMN model translation

Cited by 5 publications

References 39 publications

A Software Testing Workflow Analysis Tool Based on the ADCV Method

A Software Testing Workflow Analysis Tool Based on the ADCV Method

Solving Regression Problems with Intelligent Machine Learner for Engineering Informatics

Selected Aspects of Interactive Feature Extraction

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