A Review of Big Data Resource Management: Using Smart Grid Systems as a Case Study

Fawad, KhanMuhammad; AzamMuhammad,; Asghar, KhanMuhammad; AlgarniFahad,; AshfaqMujaddad,; Ahmad, Ibtihaj; UllahInsaf,

doi:10.1155/2021/3740476

Cited by 12 publications

(3 citation statements)

References 111 publications

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“…1 is depicted to represent the relations among stages of the big data value chain, characteristics of big data, and stage-related popular technologies and tools. Moreover, to the best of my knowledge, characteristics of big data are generally represented standalone in big data research [40][34] [41]. However, in Fig.…”

Section: Big Data Value Chain Modelmentioning

confidence: 99%

A Comparative Perspective on Technologies of Big Data Value Chain

Aydin

2023

IEEE Access

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Data is one of the most valuable assets in the digital era because it may conceal hidden valuable insights. Diverse organizations in diverse domains overcome the challenges of the big data value chain by employing a wide range of technologies to meet their needs and achieve a variety of goals to support their decision-making. Due to the significance of data-oriented technologies, this paper presents a model of the big data value chain based on technologies used in the acquisition, storage, and analysis of data. The following are the paper's contributions: First, a model of the big data value chain is developed to illustrate a comprehensive representation of the big data value chain that depicts the relationships between the characteristics of big data and the technologies associated with each category. Second, in contrast to previous research, this paper presents an overview of technologies for each category of the big data value chain. The third contribution of this paper is to assist researchers and developers of data-intensive systems in selecting the appropriate technology for their specific application development use cases by providing examples of applications and use cases from prominent papers in a variety of fields and by describing the capabilities and stages of the technologies being presented so that the right technology is used at the right time in the big data collection, processing, storage, and analytics tasks.

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Section: Big Data Value Chain Modelmentioning

confidence: 99%

A Comparative Perspective on Technologies of Big Data Value Chain

Aydin

2023

IEEE Access

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“…With the popularization and application of the concept of the smart grid, the danger of the data silo issue in the smart grid has become more and more prominent, and an effective way to solve this issue is to establish a reasonable and effective data-sharing model [1]. In today's society, with the rapid development of science and technology, the interconnection of data has become an important measure with which to enhance scientific and technological innovation in all walks of life and even the country, and many studies have shown that the interoperability and sharing of data can improve the utilization rate of data resources [2][3][4][5], accelerate the dissemination of data resources, and provide a very convenient way for people's work, life, and travel. However, in the process of data sharing, users' unwillingness to share data is affected by the formation of a mutual trust relationship and economic utility.…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Incentive Mechanism for Smart Grid Data Sharing Based on Evolutionary Game Theory

Zhang,

Lu,

Huang

et al. 2023

Energies

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With the increasing popularization and application of the smart grid, the harm of the data silo issue in the smart grid is more and more prominent. Therefore, it is especially critical to promote data interoperability and sharing in the smart grid. Existing data-sharing schemes generally lack effective incentive mechanisms, and data holders are reluctant to share data due to privacy and security issues. Because of the above issues, a dynamic incentive mechanism for smart grid data sharing based on evolutionary game theory is proposed. Firstly, several basic assumptions about the evolutionary game model are given, and the evolutionary game payoff matrix is established. Then, we analyze the stabilization strategy of the evolutionary game based on the payoff matrix, and propose a dynamic incentive mechanism for smart grid data sharing based on evolutionary game theory according to the analysis results, aiming to encourage user participation in data sharing. We further write the above evolutionary game model into a smart contract that can be invoked by the two parties involved in data sharing. Finally, several factors affecting the sharing of data between two users are simulated, and the impact of different factors on the evolutionary stabilization strategy is discussed. The simulation results verify the positive or negative incentives of these parameters in the data-sharing game process, and several factors influencing the users’ data sharing are specifically analyzed. This dynamic incentive mechanism scheme for smart grid data sharing based on evolutionary game theory provides new insights into effective incentives for current smart grid data sharing.

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“…The rapid development of affordable technology, widespread use of the internet, and the development of smart and portable devices that can connect to the internet via wireless or cellular networks, as well as the increasing number of different sectors and individuals utilizing these technologies, have significantly increased the amount of data generated in various sizes, formats, speeds, and types (Khan et al, 2021). Moreover, this digital transformation has triggered the generation of data and profoundly altered people's daily activities and habits as a result of the implementation of various applications in education, finance, health care, business, commerce, manufacturing, retail, and government services to facilitate daily activities (Dutta & Jayapal, 2016;Kejariwal et al, 2017;Syed et al, 2021;Tang et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Real-Time Big Data Processing and Analytics: Concepts, Technologies, and Domains

KEKEVİ>

Aydin

2022

AB-BBD

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In the digital era, data is one of the most important assets since it conceals valuable information. Developers of data-intensive systems have new challenges at each level of streaming, storing, and processing large quantities of data in a variety of forms and speeds. Obtaining useful information at the proper time and place is also crucial. Since the value of information is inversely proportional to time, real-time data processing and analytics are receiving more attention. Due to the importance of real-time data processing and analytics, this study focuses on real-time data processing concepts and terminology, popular technologies used in real-time data processing and analytics, popular NoSQL storage technologies used in real-time data processing, and real-time data processing application areas. The purpose of this paper is to provide researchers of real-time analysis and developers of data-intensive systems with a comparative perspective on real-time data processing by highlighting the key characteristics of real-time data processing technologies, NoSQL storage technologies, their application domains, and selected examples from previous studies.

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A Review of Big Data Resource Management: Using Smart Grid Systems as a Case Study

Cited by 12 publications

References 111 publications

A Comparative Perspective on Technologies of Big Data Value Chain

A Comparative Perspective on Technologies of Big Data Value Chain

A Dynamic Incentive Mechanism for Smart Grid Data Sharing Based on Evolutionary Game Theory

Real-Time Big Data Processing and Analytics: Concepts, Technologies, and Domains

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