Hybrid approach for big data localization and semantic annotation

Ramay, Waheed Yousuf; Cheng-Yin, Xu; Rahman, Shams; Habib, Muhammad Asif

doi:10.1002/cpe.4955

Cited by 2 publications

(3 citation statements)

References 13 publications

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“…Approach for Big Data Localization and Semantic Annotation,'' is aimed to compare and map data models used for transformation between NoSQL, RDB, and Semantic Web. 6 This study will help in achieving much better and enhanced technology-based systems for retrieval and storage of data among Big-data annotation using Semantic Web. It is aimed to reduce the response time of queries and offer compatibility with the web and semantically enriched data format.…”

Section: Modelsmentioning

confidence: 97%

“…Many solutions like tools and languages are provided in the shape of the transformation of data from RDB to RDF. The contribution by Ramay et al, “Hybrid Approach for Big Data Localization and Semantic Annotation,” is aimed to compare and map data models used for transformation between NoSQL, RDB, and Semantic Web 6 . This study will help in achieving much better and enhanced technology‐based systems for retrieval and storage of data among Big‐data annotation using Semantic Web.…”

Section: Themes Of This Special Issuementioning

confidence: 99%

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Special issue on real‐time behavioral monitoring in IoT applications using big data analytics

Jeon

Chehri

Cuomo

et al. 2019

Concurrency and Computation

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Real-time social multimedia level threat monitoring is becoming harder, due to higher and rapidly increasing data induction. Data induction through electric smart devices is greater compared to information processing capacity. Nowadays, data becomes humongous even coming from the single source. Therefore, when data emanates from all heterogeneous sources distributed over the globe makes data magnitude harder to process up to a needed scale. Big data and Deep learning have become standard in providing well-known solutions built-up using algorithms and techniques in resolving data matching issues. Now, with the involvement of sensors and automation in generating data obscures everything, predicting results to overcome a current era of ever enhancing demands and getting real-time visualization brings the need of feature like human behavior mode extraction to overcome any future threats. Big data analytics can bring the opportunity of predicting any misfortune even before they happen. Map reduce feature of big data supports massive data oriented process execution using distributed processing. Real-time human feature identification and detection can occur through sensors and internet sources. A behavioral prediction can further classify the information collected for introducing enhanced security extents. Real-time sensor devices are producing 24/7-hour data for further processing recording each event. IoT-based sensors can support in behavioral analysis model of a human. Real-time human behavioral monitoring based on image processing and IoT using big data analytics.

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Section: Modelsmentioning

confidence: 97%

Section: Themes Of This Special Issuementioning

confidence: 99%

Special issue on real‐time behavioral monitoring in IoT applications using big data analytics

Jeon

Chehri

Cuomo

et al. 2019

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…A research study was provided by Ramay et al (2021), in which the authors offered data models for the purpose of comparing and mapping NoSQL, RDB, and Semantic Web for the purpose of transformation [38]. This study was presented as part of a research study.…”

Section: Related Workmentioning

confidence: 99%

Orchestrating Distributed Computing and Web Technology with Semantic Web and Big Data

Amanoul,

Abdulrahman,

Abdullah

et al. 2023

Journal of Smart Internet of Things

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Complex data systems are incapable of processing large data volumes, rendering the task of retrieving pertinent information unattainable. The advent of the Internet has amplified the significance of accessible and readily available information. Additionally, it receives support from the World Wide Web Consortium (W3C) and global organizations responsible for establishing web standards, such as Web Ontology, Inc. It expands the functionality of the website to facilitate the retrieval, integration, and transmission of information. In recent years, several major organizations have shown a strong inclination towards using semantic technologies for the purpose of collecting Big Data. Undoubtedly, there are other advantages of integrating this into the Creative. It enhances the ability of end-users to manage data from many repositories, focuses on changing the corporate environment and the user experience, and incorporates individual definitions and integrates several data sources. Furthermore, the market’s evolving expectations and contemporary organizational practices require an adaptable but all-encompassing information strategy. Integration of data warehousing may be achieved by the use of scattered corporate ontologies. This study explores the impact of the Semantic Web on enhancing the intelligence of Big Data. It analyses the obstacles and opportunities associated with the integration of Big Data with the Semantic Web.

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Hybrid approach for big data localization and semantic annotation

Cited by 2 publications

References 13 publications

Special issue on real‐time behavioral monitoring in IoT applications using big data analytics

Special issue on real‐time behavioral monitoring in IoT applications using big data analytics

Orchestrating Distributed Computing and Web Technology with Semantic Web and Big Data

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