Multiperspective Assessment of Enterprise Data Storage Systems: Literature Review

Shrestha, Luja; Sheikh, Nasir Jamil

doi:10.23919/picmet53225.2022.9882537

Cited by 3 publications

(5 citation statements)

References 7 publications

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“…The paper underscores the challenges posed by increasing regulation in the era of big data and globalization and suggests that data analytics, including quantum computing, can play a pivotal role in addressing these challenges. Shrestha and Sheikh (2022) provide a multiperspective assessment of enterprise data storage systems, analyzing emerging technologies and current trends. Their literature review considers strategic, operational, and regulatory perspectives in assessing data storage systems.…”

Section: Challenges and Limitations In Quantum Data Processingmentioning

confidence: 99%

Quantum Computing in Big Data Analytics: A Comprehensive Review: Assessing the Advancements, Challenges, and Potential Implications of Quantum Approaches in Handling Massive Data Sets

Akoh Atadoga,

Ogugua Chimezie Obi,

Femi Osasona

et al. 2024

Comput. sci. IT res. j.

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This study provides a comprehensive review of the advancements, challenges, and potential implications of quantum computing in the field of big data analytics. The primary objective is to assess how quantum computing paradigms are transforming data processing and analysis, with a focus on their application across various sectors, including healthcare, finance, and scientific research. Employing a systematic literature review and content analysis, the study analyzes peer-reviewed articles, conference proceedings, and academic journals from databases such as PubMed, IEEE Xplore, and ScienceDirect. Key findings reveal that quantum computing, with its advanced algorithms and machine learning techniques, offers significant improvements in computational speed and efficiency over classical computing methods. This technological advancement enables the handling of large and complex datasets, presenting new opportunities in data analytics. However, the study also identifies challenges such as scalability, error correction, and integration with existing systems, which currently limit the full potential of quantum computing in big data analytics. The study concludes with strategic recommendations for industry leaders and policymakers, emphasizing the need for investment in research and development, the establishment of regulatory frameworks, and the development of educational programs to support this emerging field. Future research directions are suggested, focusing on overcoming technological limitations and exploring the long-term implications of quantum computing in various industries. This study contributes valuable insights into the evolving landscape of quantum computing and its significant impact on big data analytics. Keywords: Quantum Computing, Big Data Analytics, Advanced Algorithms, Data Processing.

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Section: Challenges and Limitations In Quantum Data Processingmentioning

confidence: 99%

Quantum Computing in Big Data Analytics: A Comprehensive Review: Assessing the Advancements, Challenges, and Potential Implications of Quantum Approaches in Handling Massive Data Sets

Akoh Atadoga,

Ogugua Chimezie Obi,

Femi Osasona

et al. 2024

Comput. sci. IT res. j.

View full text Add to dashboard Cite

show abstract

“…Regulatory frameworks play a crucial role in shaping the evolution of data storage technologies. Shrestha and Sheikh (2022) emphasize the importance of considering regulatory perspectives when assessing enterprise data storage systems (EDSS). Their multiperspective assessment model, which includes strategic, technological, operational, regulatory, and economic aspects, highlights the need for comprehensive decision models that take into account the complex regulatory environment surrounding data storage.…”

Section: The Role Of Regulatory Framework In Data Storage Evolutionmentioning

confidence: 99%

“…Regulatory frameworks and market dynamics significantly influence emerging trends and innovations in data storage technologies. Shrestha and Sheikh (2022) identify current trends and emerging technologies in data storage, emphasizing the need for models that can adapt to the evolving technological landscape while complying with regulatory requirements. Darvishi, Liu, and Lim (2022) discuss the transformative potential of emerging technologies across industries, highlighting the need for integrative approaches that balance innovation with responsible practices.…”

Section: Emerging Trends and Innovations In Data Storage Technologiesmentioning

confidence: 99%

“…The role of regulatory frameworks in data storage evolution and the emerging trends and innovations in data storage technologies are deeply interconnected. The studies by Shrestha and Sheikh (2022), Darvishi, Liu, and Lim (2022), Spousta and Chan (2015), and Chen and Rodero (2017) highlight the need for adaptive and comprehensive approaches that consider the legal, economic, and technological aspects of data storage. As these technologies continue to evolve, understanding and navigating the complex regulatory landscape will be crucial for fostering innovation and ensuring the responsible use of data storage technologies.…”

Section: Emerging Trends and Innovations In Data Storage Technologiesmentioning

confidence: 99%

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Innovation in Data Storage Technologies: From Cloud Computing to Edge Computing

Odunayo Josephine Akindote,

Abimbola Oluwatoyin Adegbite,

Samuel Onimisi Dawodu

et al. 2023

Comput. sci. IT res. j.

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In an era where data is the new gold, understanding the evolution and future trajectory of data storage technologies is crucial. This paper delves into the transformative journey from traditional storage methods to contemporary paradigms like cloud and edge computing, underpinned by the burgeoning influence of Big Data, IoT, AI, and machine learning. The study's aim is to provide a comprehensive analysis of these technologies, assessing their development, efficacy, and the challenges they face in meeting the escalating demands of data storage. The methodology employed is a meticulous synthesis of literature reviews, case studies, and comparative analyses. This approach facilitates an in-depth exploration of the historical evolution of data storage, the paradigm shifts from cloud to edge computing, and the interplay between technological advancements and user demands. The study also scrutinizes the security concerns inherent in these technologies and identifies strategic directions for future research. Key findings reveal that while cloud computing has revolutionized data storage with its scalability and flexibility, edge computing emerges as a vital solution to latency and bandwidth limitations. The integration of AI and machine learning is identified as a pivotal factor in enhancing the efficiency and intelligence of data storage systems. However, this integration presents unique challenges, necessitating innovative solutions. Conclusively, the study recommends a continued focus on innovation in data storage technologies, emphasizing the development of integrated, secure, and efficient solutions. Future research should particularly explore the potential of AI and machine learning in overcoming current limitations. The paper's scope encompasses a comprehensive overview of the current state and future potential of data storage technologies, making it a valuable resource for researchers, technologists, and policymakers in the field. Keywords: Data Storage Technologies, Cloud Computing, Edge Computing, Big Data, Internet of Things (IoT), Artificial Intelligence (AI).

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“…Specifically, the integration of IP technology [45][46][47][48] at the transport layer has significantly improved the compatibility of FC networks with traditional networks, enabling dynamic adaptation of transmissions. Simultaneously, the combination of FC technology with mass storage technology [49][50][51][52][53][54][55] at the application layer has provided support for the seamless integration of high-speed sensing, transmission, storage, and computing, facilitating a unified fusion of these functions. This paper will delve into an innovative approach toward the fusion of multiple heterogeneous technologies, aiming to seamlessly integrate the space FC-AE-1553 network with optical wireless networks.…”

mentioning

confidence: 99%

Optical Wireless Fronthaul-Enhanced High-Throughput FC-AE-1553 Space Networks

Chang,

Li,

et al. 2023

Photonics

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Existing space application networks in space stations are mainly fiber-optic cable-based networks due to their low size, weight, and power (SWaP) values. While fiber networks in space stations offer data transmission at high speeds with minimal signal loss, their major disadvantage is the lack of flexibility and mobility when new and unplanned space scientific equipment is added to the network. To enhance the flexibility of space networks while increasing their throughput, this paper introduces the hybrid space network (HSN), a new space network architecture that incorporates an optical wireless link, to meet the ever-increasing demands for larger bandwidth and higher mobile access capabilities in space scientific experiments. To best utilize the HSN’s system performance, we propose a multi-priority-based network scheduling scheme, which can dynamically adapt to the requirements of mass tasks and select the best transmission procedure. Through simulations, we find that by adding optical wireless communication (OWC) links to the state-of-the-art deterministic FC-AE-1553 space network, the HSN’s bandwidth can be increased by 20 times with an average latency reduction of 87.3%. We believe that the proposed HSN’s architecture may ultimately shape the future of space stations’ wireless connectivity, and in the meantime, innovate many advanced space applications with larger data rates and mobility requirements.

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Multiperspective Assessment of Enterprise Data Storage Systems: Literature Review

Cited by 3 publications

References 7 publications

Quantum Computing in Big Data Analytics: A Comprehensive Review: Assessing the Advancements, Challenges, and Potential Implications of Quantum Approaches in Handling Massive Data Sets

Quantum Computing in Big Data Analytics: A Comprehensive Review: Assessing the Advancements, Challenges, and Potential Implications of Quantum Approaches in Handling Massive Data Sets

Innovation in Data Storage Technologies: From Cloud Computing to Edge Computing

Optical Wireless Fronthaul-Enhanced High-Throughput FC-AE-1553 Space Networks

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