Strategies for Leveraging Big Data and Analytics for Business Development: A Comprehensive Review Across Sectors

Nneka Adaobi Ochuba,; Olukunle Oladipupo Amoo,; Enyinaya Stefano Okafor,; Olatunji Akinrinola,; Favour Oluwadamilare Usman,

doi:10.51594/csitrj.v5i3.861

Cited by 3 publications

References 49 publications

(51 reference statements)

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A State-of-the-Art Review in Big Data Management Engineering: Real-Life Case Studies, Challenges, and Future Research Directions

Theodorakopoulos,

Theodoropoulou,

Stamatiou

2024

Eng

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The explosion of data volume in the digital age has completely changed the corporate and industrial environments. In-depth analysis of large datasets to support strategic decision-making and innovation is the main focus of this paper’s exploration of big data management engineering. A thorough examination of the basic elements and approaches necessary for efficient big data use—data collecting, storage, processing, analysis, and visualization—is given in this paper. With real-life case studies from several sectors to complement our exploration of cutting-edge methods in big data management, we present useful applications and results. This document lists the difficulties in handling big data, such as guaranteeing scalability, governance, and data quality. It also describes possible future study paths to deal with these issues and promote ongoing creativity. The results stress the need to combine cutting-edge technology with industry standards to improve decision-making based on data. Through an analysis of approaches such as machine learning, real-time data processing, and predictive analytics, this paper offers insightful information to companies hoping to use big data as a strategic advantage. Lastly, this paper presents real-life use cases in different sectors and discusses future trends such as the utilization of big data by emerging technologies.

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A State-of-the-Art Review in Big Data Management Engineering: Real-Life Case Studies, Challenges, and Future Research Directions

Theodorakopoulos,

Theodoropoulou,

Stamatiou

2024

Eng

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Machine Learning Algorithms for Predictive Pest Modeling in Agricultural Crops

Mustafa,

Liu,

Zheng

et al. 2024

Advances in Environmental Engineering and Green Technologies

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Food security and maximum yield depend on accurate pest prediction and crop management. An in-depth analysis of this cutting-edge area is the goal of this book chapter, which will explore predictive pest modeling using machine learning (ML) algorithms. The introduction establishes the section by stressing the significance of ML in transforming crop pest management and the value of predictive pest modeling. Furthermore, it will delve into various ML techniques designed for pest modeling. Differentiating between supervised, unsupervised, and semi-supervised learning techniques, it will outline a range of ML methods. Moreover, to help practitioners make an educated decision, it will also focus on the criteria for algorithm selection in pest prediction. It concludes with a detailed overview of ML algorithms' revolutionary potential in agricultural operations and their importance in predictive pest modeling.

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Application of Big Data Analysis and Cloud Computing Technology

Guo

2024

International Journal of Grid and High Performance Computing

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While networks bring convenience to people, more attention must be paid to the security of the network platform. This study combines big-data technology and machine learning (ML) to investigate the application of big-data analysis and cloud-computing technology in network security. First, the data-collection technology of abnormal network behavior is introduced, and the Flume data-collection component and Kafka distributed technology are discussed. Second, the data-processing process of abnormal network behavior and the corresponding algorithm processing are analyzed. Finally, an abnormal network behavior detection model based on big data is constructed and compared with related models based on decision-tree and random-forest (RF) algorithms. The experimental results show that the big-data anomalous network behavior detection technology based on the ML framework can effectively improve the type and efficiency of anomalous network behavior detection, which is of some significance for improving the network security control capability.

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Strategies for Leveraging Big Data and Analytics for Business Development: A Comprehensive Review Across Sectors

Cited by 3 publications

References 49 publications

A State-of-the-Art Review in Big Data Management Engineering: Real-Life Case Studies, Challenges, and Future Research Directions

A State-of-the-Art Review in Big Data Management Engineering: Real-Life Case Studies, Challenges, and Future Research Directions

Machine Learning Algorithms for Predictive Pest Modeling in Agricultural Crops

Application of Big Data Analysis and Cloud Computing Technology

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