Semantic network of ICT domains and applications

Muhamedyev, Ravil I.; Kalimoldaev, Maksat N.; Uskenbayeva, Raissa

doi:10.1145/2729104.2729112

Cited by 11 publications

(3 citation statements)

References 13 publications

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“…The continuing increases in processing power, storage capacity, the use of information standards, and agreements between different data providers are factors that contribute to the growth of big data in healthcare environments. Thus, there is the possibility of accessing heterogeneous and distributed clinical information to obtain patterns that make it possible to address existing problems, as well as anticipating and preventing their origin [ 37 ].…”

Section: Resultsmentioning

confidence: 99%

Healthcare Information Technology: A Systematic Mapping Study

et al. 2023

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Objectives: This paper presents a systematic mapping of studies related to information systems and technology in the field of healthcare, enabling a visual mapping of the different lines of knowledge that can provide an overview of the scientific literature in this field. This map can help to clarify critical aspects of healthcare informatics, such as the main types of information systems, the ways in which they integrate with each other, and the technological trends in this field. Methods: Systematic mapping refers to a process of classifying information in a given area of knowledge. It provides an overview of the state of the art in a particular discipline or area of knowledge, establishing a map that describes how knowledge is structured in that particular area. In this study, we proposed and carried out a specific implementation of the methodology for mapping. In total, 1,619 studies that combine knowledge related to information systems, computer science, and healthcare were selected and compiled from prestigious publications. Results: The results established a distribution of the available literature and identified papers related to certain research questions, thereby providing a map of knowledge that structures the different trends and main areas of research, making it possible to address the research questions and serving as a guide to deepen specific aspects of the field of study. Conclusions: We project and propose future research for the trends that stand out because of their interest and the possibility of exploring these topics in greater depth.

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Section: Resultsmentioning

confidence: 99%

Healthcare Information Technology: A Systematic Mapping Study

et al. 2023

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“…A new article [55] offered a 'Disruptive Networking' strategy, which enables many IoT nodes to take part in autonomous and cooperative decision-making. Other reports [56] explore methods of transitioning the healthcare and renewable energy industries from e-government to smart government.…”

Section: Review and Survey Articlesmentioning

confidence: 99%

From 5G to 6G Technology: Meets Energy, Internet-of-Things and Machine Learning: A Survey

Mahdi

Ahmad

Qassim³

et al. 2021

Applied Sciences

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Due to the rapid development of the fifth-generation (5G) applications, and increased demand for even faster communication networks, we expected to witness the birth of a new 6G technology within the next ten years. Many references suggested that the 6G wireless network standard may arrive around 2030. Therefore, this paper presents a critical analysis of 5G wireless networks’, significant technological limitations and reviews the anticipated challenges of the 6G communication networks. In this work, we have considered the applications of three of the highly demanding domains, namely: energy, Internet-of-Things (IoT) and machine learning. To this end, we present our vision on how the 6G communication networks should look like to support the applications of these domains. This work presents a thorough review of 370 papers on the application of energy, IoT and machine learning in 5G and 6G from three major libraries: Web of Science, ACM Digital Library, and IEEE Explore. The main contribution of this work is to provide a more comprehensive perspective, challenges, requirements, and context for potential work in the 6G communication standard.

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“…<<The Semantic Web will enable machines to comprehend semantic documents and data, not human speech and writings>> Tim Berners Lee [52]. Figure 6 clearly shows the fascinating growth of Big Data publications over the decades and the emergence of semantic technologies which increase respectively [49,53].…”

Section: Using Ontologies and Semantic Web With Big Datamentioning

confidence: 99%

Semantic Integration in Big Data: State-of-the-Art

Sayah

Kazar

Ghenabzia

2020

JMM

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Nowadays, web users and systems continually overload the web with an exponential generation of a massive amount of data. This leads to making big data more important in several domains such as social networks, internet of things, health care, E-commerce, aviation safety, etc. The use of big data has become increasingly crucial for companies due to the significant evolution of information providers and users on the web. However, big data remain meaningless without semantics. In order to get a good comprehension of big data, we raise questions about how big data and semantic are related to each other and how semantic may help. To overcome this problem, researchers devote considerable time to the integration of ontology in big data to ensure reliable interoperability between systems in order to make big data more useful, readable and exploitable. This technology can hide the heterogeneity of different data resources. Moreover, in given domains, users can exchange knowledge without caring to choose the suitable semantic that makes their content more expressive. This paper aims to provide a comprehensive overview for readers about big data and the appropriate tools to manipulate and analyse them such as Hadoop. Afterwards, we talk about ontology and how it can be used to improve big data management and analyses for decision makers. Finally, different semantic integration approaches are seen in a comparative study. This survey is concluded with a discussion and some perspectives.

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Semantic network of ICT domains and applications

Cited by 11 publications

References 13 publications

Healthcare Information Technology: A Systematic Mapping Study

Healthcare Information Technology: A Systematic Mapping Study

From 5G to 6G Technology: Meets Energy, Internet-of-Things and Machine Learning: A Survey

Semantic Integration in Big Data: State-of-the-Art

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