Dynamis: Effective Context-Aware Web Service Selection Using Dynamic Attributes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Quality web service discovery requires narrowing the search space from an overwhelming set of services down to the most relevant ones, while matching the consumer’s request. Today, the ranking of services only considers static attributes or snapshots of current attribute values, resulting in low-quality search results. To satisfy the user’s need for timely, well-chosen web services, we ought to consider quality of service attributes. The problem is that dynamic attributes can be difficult to measure, frequently fluctuate, are context-sensitive and depend on environmental factors, such as network availability at query time. In this paper, we propose the Dynamis algorithm to address these challenges effectively. Dynamis is based on well-established database techniques, such as skyline and aggregation. We illustrate our approach using observatory telescope web services and experimentally evaluate it using stock market data. In our evaluation, we show significant improvement in service selection over existing techniques.
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.
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 | 0.001 |
| 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 it