The Case for an Adaptive Integration Framework for Data Aggregation/Dissemination in Service-Oriented Architectures
Bibliographic record
Abstract
The migration to Service Oriented Architectures (SOA) implies many real-time applications distributed across large geographic areas with highly mobile users and sensors that require exchange of critical data among local as well as distant users across resource constrained networks. These emerging applications can be characterized as distributed collaborative adaptive systems. They are likely to rely on ad hoc wireless networks particularly in military and emergency response applications for transport of critical information and in many cases in multimedia form. Users of these systems are likely to have different needs or views of sensor data either because of organizational role or geographic location. In this distributed architecture, available resources must dynamically reconfigure themselves to respond to external factors such as changes in the environment, changes in short-term objectives, reallocation of responsibilities, and changes in information flow patterns. This paper describes a framework for dynamic resource management (DRM) and Quality of Service (QoS) in support of network aware applications and resiliency in ad hoc delay tolerant networking (DTN). The proposed framework is based on managing perflow, end-to-end provisioning of heterogeneous network resources in support of mission-driven resource management.
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 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 | 0.001 | 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 | 0.000 |
| 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".