A Cost-Effective Framework for Running Industrial Big Data Analysis Applications in Public Clouds

Lin, Liduo; Pan, Li; Liu, Shijun

doi:10.1109/jiot.2021.3122196

Cited by 4 publications

(3 citation statements)

References 40 publications

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“…The first class has articles discussing business processes (Zhang et al 2022a;Kong et al 2022;Yang and Ge 2022;Shafi et al 2021;Krumeich et al 2014;Mishra 2019). The second class has articles focusing on the Industrial Internet of things (IIoT) (Failed 2017c(Failed , 2018bYu et al 2020;Wang et al 2020;Tryapkin and Shurova 2020;Lin et al 2022;Kodidala et al 2021;Rosati, et al 2023). The third one deals with articles that talk about issues in supply-chain management (Hazen et al 2014;Gunasekaran, et al 2017;Dubey, et al 2019;Jeble 2018;Nilashi, et al 2023) and the pros of big data analysis and precise prediction in this domain.…”

Section: Industrial Applicationsmentioning

confidence: 99%

“…Lin, et al (Lin et al 2022) proposed a per-job framework in order to lower the costs and energy of data analysis. In aiming to reduce any possible loss of spot instances irregularities, the authors used a checkpointing mechanism.…”

Section: Overview Of Industrial Articlesmentioning

confidence: 99%

“…These improvements will positively affect resource reliability, utilization, and other related metrics. According to the reviewed and discussed industrial articles, the comparison of their specifications has been depicted in Table 3 which are divided into three categories: business processes (Zhang et al 2022a;Kong et al 2022;Yang and Ge 2022;Shafi et al 2021;Krumeich et al 2014;Mishra 2019);IIoT (Failed 2017c, 2018bYu et al 2020;Wang et al 2020;Tryapkin and Shurova 2020;Lin et al 2022;Kodidala et al 2021); Supply chain (Hazen et al 2014;Gunasekaran, et al 2017;Dubey, et al 2019;Jeble 2018;Nilashi, et al 2023). Table 3 shows the main ideas, evaluation methods, tools, advantages, and disadvantages of industrial articles.…”

Section: Summary Of Industrial Articlesmentioning

confidence: 99%

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Big data and predictive analytics: A systematic review of applications

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Haddadi,

Sarvizadeh

et al. 2024

Artif Intell Rev

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Big data involves processing vast amounts of data using advanced techniques. Its potential is harnessed for predictive analytics, a sophisticated branch that anticipates unknown future events by discerning patterns observed in historical data. Various techniques obtained from modeling, data mining, statistics, artificial intelligence, and machine learning are employed to analyze available history to extract discriminative patterns for predictors. This study aims to analyze the main research approaches on Big Data Predictive Analytics (BDPA) based on very up-to-date published articles from 2014 to 2023. In this article, we fully concentrate on predictive analytics using big data mining techniques, where we perform a Systematic Literature Review (SLR) by reviewing 109 articles. Based on the application and content of current studies, we introduce taxonomy including seven major categories of industrial, e-commerce, smart healthcare, smart agriculture, smart city, Information and Communications Technologies (ICT), and weather. The benefits and weaknesses of each approach, potentially important changes, and open issues, in addition to future paths, are discussed. The compiled SLR not only extends on BDPA’s strengths, open issues, and future works but also detects the need for optimizing the insufficient metrics in big data applications, such as timeliness, accuracy, and scalability, which would enable organizations to apply big data to shift from retrospective analytics to prospective predictive if fulfilled.

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