Trustworthy Artificial Intelligence and Process Mining: Challenges and Opportunities

Pery, Andrew; Rafiei, Majid; Simon, Michael A.; Aalst, Wil M. P. van der

doi:10.1007/978-3-030-98581-3_29

Cited by 6 publications

(5 citation statements)

References 13 publications

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“…(3) timing performance anomaly detection, in addition to control-flow anomaly detection [7]; (4) introduction of process discovery to automatically generate run-time models [7]; and (5) tackling of trustworthiness and explainability challenges within process mining, machine learning, and digital twins [42].…”

Section: Discussionmentioning

confidence: 99%

Digital Twins for Anomaly Detection in the Industrial Internet of Things: Conceptual Architecture and Proof-of-Concept

Benedictis

Flammini

Mazzocca

et al. 2023

IEEE Trans. Ind. Inf.

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Modern cyber-physical systems based on the Industrial Internet of Things (IIoT) can be highly distributed and heterogeneous, and that increases the risk of failures due to misbehavior of interconnected components, or other interaction anomalies. In this paper, we introduce a conceptual architecture for IIoT anomaly detection based on the paradigms of Digital Twins (DT) and Autonomic Computing (AC), and we test it through a proof-of-concept of industrial relevance. The architecture is derived from the current state-of-the-art in DT research and leverages on the MAPE-K feedback loop of AC in order to monitor, analyze, plan, and execute appropriate reconfiguration or mitigation strategies based on the detected deviation from prescriptive behavior stored as shared knowledge. We demonstrate the approach and discuss results by using a reference operational scenario of adequate complexity and criticality within the European Railway Traffic Management System.

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Section: Discussionmentioning

confidence: 99%

Digital Twins for Anomaly Detection in the Industrial Internet of Things: Conceptual Architecture and Proof-of-Concept

Benedictis

Flammini

Mazzocca

et al. 2023

IEEE Trans. Ind. Inf.

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“…In particular, this applies to the increasingly used and important intelligent IS containing AI elements. The lack of data, their fragmentation, or inconsistency with the actual course of business processes immediately increases the risk of incorrect learning for the AI, and thus of errors in the support of managers by IS (Pery et al, 2022). The consequence is that managers make and implement wrong decisions that have negative economic consequences (Mariani et al, 2023).…”

Section: Industry 40 Requirements and Opportunities For Business Proc...mentioning

confidence: 99%

“…Thanks to the possibility of analysing data outside of SI logs themselves, it also enables the analysis of not only traditional, predictable business processes, but also unpredictable and often unique business processes that require dynamic management. By offering access to a broad spectrum of reliable data, which is not the result of subjective selection, the WPM may be a source of data for any AI methodologies using ML to improve business processes (Pery et al, 2022).…”

Section: Functionality Of the Wpm Toolmentioning

confidence: 99%

Workplace performance measurement: digitalization of work observation and analysis

Nesterak,

Szelągowski,

Radziszewski

2024

J Intell Manuf

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Process improvement initiatives require access to frequently updated and good quality data. This is an extremely difficult task in the area of production processes, where the lack of a process digital footprint is a very big challenge. To solve this problem, the authors of this article designed, implemented, and verified the results of a new work measurement method. The Workplace Performance Measurement (WPM) method is focused not only on the measurement of task duration and frequency, but also on searching for potential anomalies and their reasons. The WPM method collects a wide range of workspace parameters, including workers' activities, workers' physiological parameters, and tool usage. An application of Process Mining and Machine Learning solutions has allowed us to not only significantly increase the quality of analysis (compared to analog work sampling methods), but also to implement an automated controlling solution. The genuine value of the WPM is attested to by the achieved results, like increased efficiency of production processes, better visibility of process flow, or delivery of input data to MES solutions. MES systems require good quality, frequently updated information, and this is the role played by the WPM, which can provide this type of data for Master Data as well as for Production Orders. The presented authorial WPM method reduces the gap in available scholarship and practical solutions, enabling the collection of reliable data on the actual flow of business processes without their disruption, relevant for i.a. advanced systems using AI.

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“…Market Decisions [44,45] Decision making using AI optimizing recommendations of information/products and regulatory compliance processes.…”

Section: Research Topic Topic Description Proposed Research Advances ...mentioning

confidence: 99%

“…The proposed PM model is suitable to be applied also in the strategic marketing field, by defining hierarchical risk levels according to new specific key variables as for [44,45].…”

Section: Research Topic Topic Description Proposed Research Advances ...mentioning

confidence: 99%

Multi-Level Decision Support System in Production and Safety Management

Massaro

2022

Knowledge

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The proposed paper introduces an innovative approach based on the implementation of a multi-level Decision Support System (DSS) modelling processes in the industry. Specifically, the work discusses a theoretical Process Mining (PM) DSS model gaining digital knowledge by means of logics that are able to select the best decisions. The PM model is applied to an open dataset simulating a working scenario and defining a possible safety control method based on the risk assessment. The application of the PM model provides automatic alerting conditions based on a threshold of values detected by sensors. Specifically, the PM model is applied to worker security systems characterized by the environment with a risk of emission of smoke and gases. The PM model is improved by Artificial Intelligence (AI) algorithms by strengthening information through prediction results and improving the risk analysis. An Artificial Neural Network (ANN) MultilaLayer Perceptron (MLP) algorithm is adopted for the risk prediction by achieving the good computational performance of Mean Absolute Error (MAE) of 0.001. The PM model is first sketched by the Business Process Modelling and Notation (BPMN) method, and successively executed by means of the Konstanz Information Miner (KNIME) open source tool, implementing the process-controlling risks for different working locations. The goal of the paper is to apply the theoretical PM model by means of open source tools by enhancing how the multi-level approach is useful for defining a security procedure to control indoor worker environments. Furthermore, the article describes the key variables able to control production and worker safety for different industry sectors. The presented DSS PM model also can be applied to industry processes focused on production quality.

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Trustworthy Artificial Intelligence and Process Mining: Challenges and Opportunities

Cited by 6 publications

References 13 publications

Digital Twins for Anomaly Detection in the Industrial Internet of Things: Conceptual Architecture and Proof-of-Concept

Digital Twins for Anomaly Detection in the Industrial Internet of Things: Conceptual Architecture and Proof-of-Concept

Workplace performance measurement: digitalization of work observation and analysis

Multi-Level Decision Support System in Production and Safety Management

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