Big Data, Cloud Computing, and Internet-Enabled Infrastructures for Sustainable Development
Bibliographic record
Abstract
Sustainability has become a major global concern and this necessitates the need to develop novel methods to handle the environmental, social, and economic resources in an effective manner. The combination of Big Data, cloud computing and the infrastructures enabled by the Internet provides the unprecedented opportunities to facilitate the process of sustainable development due to the real time monitoring, data-based decision making, and intelligent resource optimization. Big Data analytics and cloud computing allow the extraction of actionable insights out of very large and heterogeneous datasets that have been gathered through IoT devices and sensors and, at the same time, offer scalable, flexible, and power-efficient platforms to process and store the data. Internet-connected infrastructures such as edge and fog computing support low latency, real time control of systems that are critical to environmental management, smart cities and industrial processes. New technologies like analytics powered by AI, blockchain to manage resources in a transparent way, green cloud computing, and digital twins will also increase the efficiency, robustness, and responsibility of sustainable systems. Nonetheless, bumps on the road, including excessive energy usage, interoperability, data privacy, and adoption limitations in developing nations, still exist, however, research and innovation are ongoing to create intelligent, adaptive, and equitable solutions. This survey provides a broad picture of the principles, usage, upcoming trends, challenges, and future study directions, and states the revolutionary potential of these technologies in realizing the sustainable development objectives.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".