Big data and predictive analytics: A systematic review of applications

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,...

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Bibliographic Details
Published in:The Artificial intelligence review 2024-06, Vol.57 (7), p.176, Article 176
Main Authors: Jamarani, Amirhossein, Haddadi, Saeid, Sarvizadeh, Raheleh, Haghi Kashani, Mostafa, Akbari, Mohammad, Moradi, Saeed
Format: Article
Language:English
Subjects:
Artificial Intelligence
Big Data
Computer Science
Data analysis
Data mining
Literature reviews
Machine learning
Predictive analytics
Systematic review
Taxonomy
Citations: Items that this one cites
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Summary: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.
ISSN:1573-7462
0269-2821
1573-7462
DOI:10.1007/s10462-024-10811-5