On the Use of Process Mining and Machine Learning to Support Decision Making in Systems Design

Es-Soufi, Widad; Yahia, Esma; Roucoules, Lionel

doi:10.1007/978-3-319-54660-5_6

Cited by 10 publications

(4 citation statements)

References 24 publications

(20 reference statements)

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“…Optimization problems. When it is not feasible to find an optimal solution for uncertain problems, a solution that satisfies problem constraints may be selected, as described in [ 84 , 110 , 111 , 112 , 113 , 114 ].…”

Section: Computational Methods For Decision-making Under Uncertaintymentioning

confidence: 99%

Decision-Making under Uncertainty for the Deployment of Future Hyperconnected Networks: A Survey

Alzate-Mejía

Santos-Boada

Amazonas

2021

Sensors

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Among the several emerging dimensioning, control and deployment of future communication network paradigms stands out the human-centric characteristic that creates an intricate relationship between telematics and human activities. The hard to model dynamics of user behavior introduces new uncertainties into these systems that give rise to difficult network resource management challenges. According to this context, this work reviews several decision-making computational methods under the influence of uncertainties. This work, by means of a systematic literature review, focuses on sensor-based Internet of Things scenarios such as Smart Spaces and Industry 4.0. According to our conclusions, it is mandatory to establish a means for modeling the human behavior context in order to improve resource assignment and management.

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Section: Computational Methods For Decision-making Under Uncertaintymentioning

confidence: 99%

Decision-Making under Uncertainty for the Deployment of Future Hyperconnected Networks: A Survey

Alzate-Mejía

Santos-Boada

Amazonas

2021

Sensors

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“…Process mapping flowcharts [5,[31][32][33] Quality management Nevertheless, an added value of using systemised tools and techniques, besides their impact on process improvement and re-engineering, is their practical use in generating, analysing, and visualising data throughout the process.…”

Section: Type Tools and Techniques Referencesmentioning

confidence: 99%

Using Machine Learning in Business Process Re-Engineering

Al-Anqoudi

Al-Hamdani

Al-Badawi

et al. 2021

BDCC

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A business process re-engineering value in improving the business process is undoubted. Nevertheless, it is incredibly complex, time-consuming and costly. This study aims to review available literature in the use of machine learning for business process re-engineering. The review investigates available literature in business process re-engineering frameworks, methodologies, tools, techniques, and machine-learning applications in automating business process re-engineering. The study covers 200+ research papers published between 2015 and 2020 in reputable scientific publication platforms: Scopus, Emerald, Science Direct, IEEE, and British Library. The results indicate that business process re-engineering is a well-established field with scientifically solid frameworks, methodologies, tools, and techniques, which support decision making by generating and analysing relevant data. The study indicates a wealth of data generated, analysed and utilised throughout business process re-engineering projects, thus making it a potential greenfield for innovative machine-learning applications aiming to reduce implementation costs and manage complexity by exploiting the data’s hiding patterns. This suggests that there were attempts towards applying machine learning in business process management and improvement in general. They address process discovery, process behaviour prediction, process improvement, and process optimisation. The review suggests that expanding the applications to business process re-engineering is promising. The study proposed a machine-learning model for automating business process re-engineering, inspired by the Lean Six Sigma principles of eliminating waste and variance in the business process.

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“…They may also be combined with machine learning (ML) techniques. In particular, the authors of [30] propose an approach that uses PM techniques to extract the most frequent patterns of an observed industrial system, while ML techniques permit to allocate resources in an optimized manner based on this analysis. A use case dedicated to healthcare with data sensors has also been described in [31], but focusing on detecting anomalies with respect to patients that may reveal diseases.…”

Section: Related Workmentioning

confidence: 99%

Comparative Assessment of Process Mining for Supporting IoT Predictive Security

Hemmer

Abderrahim

Badonnel

et al. 2021

IEEE Trans. Netw. Serv. Manage.

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The growth of the Internet-of-Things (IoT) has been characterized by the large-scale deployment of sensors and connected objects. These ones are integrated with other Internet resources in order to elaborate more complex systems and applications. Security management is a major challenge for these systems due to their complexity, their heterogeneity and the limited resources of their devices. In this paper we evaluate the exploitability and performance of a process mining approach for detecting misbehaviors in such systems. We describe the considered architecture and detail its operation, from the generation of behavioral models to the detection of potential attacks. We formalize several alternative commonly-used detection methods, including elliptic envelope, support-vector machine, local outlier factor, and isolation forest techniques. After presenting a proofof-concept prototype, we quantify comparatively the benefits and limits of our process mining solution combined with data preprocessing, through extensive experiments based on different industrial datasets.

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On the Use of Process Mining and Machine Learning to Support Decision Making in Systems Design

Cited by 10 publications

References 24 publications

Decision-Making under Uncertainty for the Deployment of Future Hyperconnected Networks: A Survey

Decision-Making under Uncertainty for the Deployment of Future Hyperconnected Networks: A Survey

Using Machine Learning in Business Process Re-Engineering

Comparative Assessment of Process Mining for Supporting IoT Predictive Security

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