Overview
Big data analysis is the process of examining very large, varied, and rapidly generated datasets to uncover patterns, correlations, trends, and other insights that support understanding and decision-making. It addresses data that exceed the capacity of traditional processing tools, characterized by high volume, velocity, and variety, and relies on specialized databases, distributed computing, and analytical and machine-learning techniques to store, manage, and interpret information. Big data analysis is applied across many domains, including business, healthcare, science, finance, and public services, where it is used to model behavior, forecast outcomes, optimize operations, and reveal relationships that smaller datasets cannot expose. Effective analysis depends not only on computational methods but also on data quality, integration, governance, and attention to legal, ethical, and privacy considerations that arise when large amounts of information are collected and used. Research relevant to this topic, within the scope of Big Data Research, includes work on the databases and computing infrastructure that underpin big data, as well as analysis of the legal, marketing, and advertising issues associated with its use. This page gathers peer-reviewed, open-access research relevant to big data analysis, spanning analytical methods, supporting technologies, and the practical and governance challenges of working with large-scale data.
Research published in this journal
4 peer-reviewed articles, ranked by relevance. Each links to its DOI.
How this research is being cited
The 4 articles above have been cited 6 times in the scholarly literature. Citation data via OpenAlex and Crossref, updated Jun 2026.
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2024 · Özgür Yayınları eBooks
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2022 · International Journal of Social Science and Human Research
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2022 · Cogent Business & Management
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2022 · Cogent Business & Management
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2019 · Advances in intelligent systems and computing
A sample of recent works citing this journal's research on Big Data Analysis, linking to each citing work.