Context-aware workflow management for smart manufacturing: A literature review of semantic web-based approaches

Ochoa, William; Larrinaga, Félix; Pérez, Alain

doi:10.1016/j.future.2023.03.017

Cited by 18 publications

(5 citation statements)

References 47 publications

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“…In general, a context-aware system uses context to deliver information and/or services relevant to a user's task (Abowd et al, 1999). Context refers to any data that can be used to understand the current state, whether situational or locational, of an entity (Ochoa et al, 2023). Context can be categorized from both operational and conceptual perspectives.…”

Section: Recommendation System and Context-awarenessmentioning

confidence: 99%

A context-aware framework for recommendation-based adaptive user interfaces in Smart Product-Service System

Carrera-Rivera,

Larrinaga,

Lasa

et al. 2024

Preprint

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Smart Product-Service Systems (S-PSS) represent an innovative business model that integrates intelligent products with advanced digital capabilities and corresponding e-services. The User Experience (UX) within an S-PSS is heavily influenced by the individualization of services and customer empowerment. However, conventional UX analysis primarily focuses on the design stage and may not adequately respond to the evolving user needs during the usage stage and how to exploit the data surrounding the use of S-PSS. To overcome these limitations, this article introduces a practical framework for developing adaptive user interfaces within S-PSS. This framework integrates ontologies and context-aware recommendation systems, with user interactions serving as the primary data source, facilitating the development of adaptive user interfaces. One of the main contributions of this work lies on the integration of various components to achieve the creation of adaptive user interfaces for digital services. A case study of a smart device app is presented, to demonstrate the practical implementation of the framework, with a hands-on development approach, considering technological aspects and utilizing appropriate tools. The results of the evaluation of the recommendation engine show that using a context-aware approach improves the precision of recommendations. Furthermore, pragmatic aspects of UX, such as usefulness and system efficiency, are evaluated with participants with an overall positive impact on the use of the smart device.

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Section: Recommendation System and Context-awarenessmentioning

confidence: 99%

A context-aware framework for recommendation-based adaptive user interfaces in Smart Product-Service System

Carrera-Rivera,

Larrinaga,

Lasa

et al. 2024

Preprint

Self Cite

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“…The complex urban difficulties brought forth by population expansion, resource restrictions, and environmental sustainability have given rise to smart cities. The development of smart cities entails the use of state-of-the-art technology to improve urban living conditions, providing inhabitants with increased sustainability, efficiency, and quality of life [21]- [25].…”

Section: Evolution Of Smart Citiesmentioning

confidence: 99%

Enhancing Smart City Services with AI: A Field Experiment in the Context of Industry 5.0

Natalia,

Kumar Joshi,

Dixit

et al. 2024

BIO Web Conf.

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The practical effects of incorporating artificial intelligence (AI) into Industry 5.0 smart city services are made evident by this empirical research. The use of AI-powered smart traffic management yields a noteworthy 32.94% rise in traffic volume, signifying a noteworthy progression towards improved urban mobility. AI waste management optimization results in a 5.71% increase in collection efficiency, highlighting the importance of operational effectiveness and resource conservation. The control of energy use shows an 8.57% decrease, confirming AI's importance in sustainable energy practices. AI-enhanced public safety offers dependable event prediction, indicating safer cityscapes. These results highlight AI's revolutionary potential and establish smart cities as safe, secure, and sustainable urban environments.

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“…Manufacturing has seen a surge in the use of artificial intelligence, especially in the areas of machine learning and deep learning. Artificial Intelligence (AI) enables systems to autonomously evaluate large datasets and make judgments, which promotes process efficiency, quality optimization, and predictive maintenance [20]- [24].…”

Section: Ai In Productionmentioning

confidence: 99%

IoT and AI Integration: An Experiment on Smart Manufacturing Efficiency in Industry 5.0

Dmitrieva,

Krishna,

Chhabra

et al. 2024

BIO Web Conf.

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In line with the Industry 5.0 paradigm, this empirical research offers a data-driven investigation of the revolutionary effects of combining IoT and AI in smart manufacturing. The findings show a notable 1.52% gain in production efficiency, which is attributed to post-implementation temperature increases of 36.2°C and humidity decreases of 44.8%. A decrease in faults found (2) led to a 0.76% increase in quality scores (93.1) for quality control. With fewer maintenance hours (2.3) and downtime (52 minutes), maintenance practices were more effective. These results highlight the concrete advantages of integrating IoT and AI in smart manufacturing, putting it at the vanguard of Industry 5.0's revolutionary landscape and improving productivity, quality, and maintenance.

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Context-aware workflow management for smart manufacturing: A literature review of semantic web-based approaches

Cited by 18 publications

References 47 publications

A context-aware framework for recommendation-based adaptive user interfaces in Smart Product-Service System

A context-aware framework for recommendation-based adaptive user interfaces in Smart Product-Service System

Enhancing Smart City Services with AI: A Field Experiment in the Context of Industry 5.0

IoT and AI Integration: An Experiment on Smart Manufacturing Efficiency in Industry 5.0

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