Customizing the Composition of Web Services and Beyond
Bibliographic record
Abstract
Web services provide a standardized means of publishing diverse, distributed applications. Increasingly, corporations are providing services or programs within and between organizations either on corporate intranets or on the cloud. Many of these services can be composed together, ideally automatically, to provide value-added service. Automated Web service composition is an example of such automation where given a specification of an objective to be realized and some knowledge of the state of the world, the problem is to automatically select, integrate, and invoke multiple services to achieve the specified objective. A popular approach to the Web service composition problem is to conceive it as an Artificial Intelligence planning task. This enables us to bring to bear many of the theoretical and computational advances in reasoning about actions to the task of Web service composition. However, Web service composition goes far beyond the reaches of classical planning, presenting a number of interesting challenges relevant to a large body of problems related to the composition of actions, programs, and services. Among these, an important challenge is generating not only a composition, but a high-quality composition tailored to user preferences.\n\nIn this thesis, we present an approach to the Web service composition problem with a particular focus on the customization of compositions. We claim that there is a correspondence between generating a customized composition of Web services and non-classical Artificial Intelligence planning where the objective of the planning problem is specified as a form of control knowledge, such as a workflow or template, together with a set of constraints to be optimized or enforced. We further claim that techniques in (preference-based) planning can provide a computational basis for the development of effective, state-of-the-art techniques for generating customized compositions of Web services.\n\nTo evaluate our claim, we characterize the Web service composition problem with customization as a non-classical planning problem, exploit and advance preference specification languages and preference-based planning, develop algorithms tailored to the Web service composition problem, prove formal properties of these algorithms, implement proof-of-concept systems, and evaluate these systems experimentally. While our research has been motivated by Web services, the theory and techniques we have developed are amenable to analogous problems in such diverse sectors as multi-agent systems, business process modeling, component software composition, and social and computational behaviour modeling and verification.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".