Composing Business Processes with Partial Observable Problem Space in Web Services Environment
Bibliographic record
Abstract
Composing business processes from individual services can be viewed as a planning problem in which a planner determines the execution orders of services in a process.Most existing Web Service composition research considers connecting Web Services into a business process. We argue that most existing Web Services are informative Web Services that are not the actual business services, but give the parameters of their correspondent business services. The planning problem is not only to select the proper business services, but also to determine the parameters of the business services which affect the ordering of the business services. Furthermore, it is not possible to extract all information from informative Web Services through queries. The planner has to work with the problem space that is not fully enumerable. This paper presents a method to optimize planning results with incompletely observed problem space. Genetic Algorithms(GA) help to navigate the incompletely observed problem space. At each loop of GA, Web Service data are queried and a new sub problem space is built. The planner works with the sub problem space and calculates all feasible plans. The plans are evaluated by GA in fitness function and the best plans are kept for the next loop of GA. The fitness function of GA reflects domain-dependent user preferences. The selected final plan is an optimized feasible plan though global optimization is not guaranteed.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".