Exploring the Evolution of Big Data Technologies: A Systematic Literature Review of Trends, Challenges, and Future Directions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study examines the evolution and impact of Big Data technologies across sectors, emphasizing key algorithms, emerging trends, and organizational challenges in their adoption. Special attention is given to ethical concerns related to data privacy, security, and scalability, underscoring the importance of responsible governance frameworks. The review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines to ensure transparency and methodological rigor. A comprehensive literature search identified 83 peer-reviewed articles from high-indexed journals, and a complementary bibliometric analysis of 1108 Scopus-sourced articles (2015–2024) was conducted using R Biblioshiny. This dual-method approach offers both qualitative depth and quantitative insights into major trends, influential sources, and leading countries in Big Data research. Key findings reveal that real-time data processing and AI integration have significantly enhanced data management capabilities, supporting faster and more informed organizational decision-making. This study concludes by highlighting the importance of ethical governance and recommending future research on sector-specific adoption patterns and strategic frameworks that maximize Big Data’s value while safeguarding privacy and trust.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it