Cloud Enabled Big Data Computing: Trends and Future Directions
Bibliographic record
Abstract
The rapid digital transformation of society has led to the generation of vast volumes of data from diverse sources, creating what is known as Big Data. Managing and extracting valuable insights from this data has become essential for achieving competitive advantage. Big Data analytics enables organizations to mine structured and unstructured data—both private and public—to understand customer behaviour, forecast demands, and optimize resources. However, implementing Big Data analytics remains complex and resource-intensive due to the need for advanced infrastructure, costly tools, and expert knowledge. Cloud computing offers a promising solution by providing scalable, elastic, and cost-effective resources for analytics through a pay-as-you-go model. This paper surveys key approaches, environments, and technologies that support Big Data analytics in cloud platforms. It highlights the benefits and challenges of integrating analytics with cloud services and discusses both technical and non-technical issues, including scalability, cost-efficiency, and governance. Finally, the paper identifies research gaps and proposes future directions for developing efficient, cloud-enabled Big Data analytics solutions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".