Full Solution Indexing Using Database for QoS-Aware Web Service Composition
Bibliographic record
Abstract
Automated service composition can fulfill user request by composing services automatically when no individual services meet the goal. Unfortunately, most of current automated service composition methods are in-memory methods, which are limited by expensive and volatile physical memory. In this work, we develop a relational-database approach for automatic service composition. Possible service combinations are stored in a relational database on persistence disk instead of volatile memory, and for any composition requests, solutions can be obtained by simple SQL queries. We offer three main contributions in this paper. First, pursuing earlier work, we overcome the disadvantages of in-memory composition algorithms, such as volatile and expensive, and provide a solution suitable to cloud environments. Second, compared with other pre-computing composition methods, we use a single SQL query: there is no need to eliminate spurious services iteratively. Third, we address the quality of services to maximize user's satisfaction in our system. An experimental validation is done, which shows the performance benefits of our system and proves that this system can find a valid composition solution with fewer services to maximize user satisfaction.
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 | 0.000 | 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 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".