Machine Learning for Big Data Analytics: Challenges, Trends, and Future Research Directions
Bibliographic record
Abstract
The rapid expansion of Big Data has revolutionized how organizations and societies create, store, and use information, but it has brought opportunities and challenges to effective intelligent data-driven decision-making. The capacity of Machine Learning (ML) to learn patterns, make predictions, and automate processes has turned it into an important key to unlocking the full potential of Big Data. This survey gives an in-depth overview of the overlap between ML and Big Data, including fundamental concepts, technology ecosystems, and a wide range of applications across the areas of healthcare, finance, smart cities, and governance. It also looks at some of the main issues like data heterogeneity, scalability, interpretability, security and privacy issues that restrict large-scale adoption. The research identifies some of the recent developments in the field such as deep learning, federated learning and edge intelligence which are transforming Big Data analytics. In addition, the paper outlines new research frontiers like sustainable and energy-efficient ML, hybrid models of heterogeneous data, human-in-the-loop systems, and cross-disciplinary applications to solve social issues such as climate change and disease epidemiology. Through a synthesis of previous work, this survey highlights the paradigm-shifting potential of ML in deriving actionable intelligence out of Big Data and describes future directions in developing scalable, secure, interpretable, and socially responsible analytics systems.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".