Data Fabrics for Multi-Domain Information Systems
Bibliographic record
Abstract
Data exchange in information systems that span multiple policy domains typically rely on network middleware that can abstract the management of underlying heterogeneous communication protocols. This also involves issues in managing interoperability, scalability, and privacy that arise in the movement of data from one domain to another information domain. The Data Fabric is an emerging approach to systematically build and design such middleware systems to support multi-domain exchange at scale. In this paper, we discuss and compare two key data-centric approaches: 1) application layer topic-based messaging and name-based networking in a multi-cloud environment. We implement and deploy these two approaches (using Kafka and NDN) and we compare the performance in terms of object transfer latency and CPU and memory utilization. We find that NDN networking has superior latency performance and lower resource usage. We believe that this advantage derives from the fact that named-based messaging operates at the network level, while topic-based messaging operates at the application level.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 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".