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

Author:

Amanoul Sandy Victor1,Abdulrahman Lozan M.2,Abdullah Rozin Majeed1,Qashi Riyadh3

Affiliation:

1. Engineering Department, Technical College of Engineering , Duhok Polytechnic University , Duhok , Iraq

2. Information Technology Department, Technical College of Administration , Duhok Polytechnic University , Duhok , Iraq

3. Vocational School Center 7, Electrical Engineering of the City of Leipzig , Laipzig , Germany

Abstract

Abstract 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.

Publisher

Walter de Gruyter GmbH

Reference50 articles.

1. Sadeeq, M. M., Abdulkareem, N. M., Zeebaree, S. R., Ahmed, D. M., Sami, A. S., & Zebari, R. R. (2021). IoT and Cloud computing issues, challenges and opportunities: A review. Qubahan Academic Journal, 1(2), 1-7.

2. Zeebaree, S. R., Shukur, H. M., Haji, L. M., Zebari, R. R., Jacksi, K., & Abas, S. M. (2020). Characteristics and analysis of Hadoop distributed systems. Technology Reports of Kansai University, 62(4), 1555-1564.

3. Abdullah, P. Y., Zeebaree, S., Jacksi, K., & Zeabri, R. R. (2020). An HRM system for small and medium enterprises (SMEs) based on cloud computing technology. International Journal of Research-GRANTHAALAYAH, 8(8), 56-64.

4. Saeed, J., & Zeebaree, S. (2021). Skin lesion classification based on deep convolutional neural networks architectures. Journal of Applied Science and Technology Trends, 2(01), 41-51.

5. Zeebaree, S., Zebari, R. R., Jacksi, K., & Hasan, D. A. (2019). Security approaches for integrated enterprise systems performance: A Review. International Journal of Science and Technology Research, 8(12), 2485-2489.

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