Bibliometric Analysis and Knowledge Mapping of Research Hotspots in Data Middle Platform Architecture
Bibliographic record
Abstract
This research employs bibliometric methods and VOSviewer software to conduct a thorough analysis of academic literature in the key technical field of data middle platform architecture. Utilizing the Web of Science Core Collection database, this study has retrieved and analyzed 625 relevant documents from 1900 to 2024, uncovering the developmental trends and academic focal points in the research of data middle platform architecture. The findings reveal that disciplines such as Computer Science and Information Systems, Health Care Sciences and Services, and Environmental Sciences have made significant contributions in this domain. Institutions like the University of Oxford (Univ Oxford), the University of Toronto (Univ Toronto), and Indiana University (Indiana Univ) are recognized for their considerable academic influence. The keyword co-occurrence network analysis highlights core topics such as "data management," "cloud computing," "big data," and "data governance," while also showcasing the potential application of emerging technologies like "machine learning," "artificial intelligence," and "blockchain" within the data middle platform architecture. The international cooperation network analysis indicates active collaboration in data middle technology research among countries including the United Kingdom (UK), Norway, Iceland, and Mexico. This study offers new perspectives and insights into understanding the academic frontier, developmental trajectory, and research hotspots of data middle platform architecture, providing a foundation for knowledge accumulation and theoretical advancement in this field.
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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 | 0.013 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.239 | 0.250 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".