A business rule explanation system for web services
Bibliographic record
Abstract
Electronic commerce has grown rapidly and brought about enormous change in business firms, markets and consumer behavior. To meet the increased demand, more and more business activities are moving to the World Wide Web because business activities can be reached everywhere and be performed more efficiently. Business rules technology is one of the most active research areas in e-commerce. It deals with representing and processing regulations and policies regarding how an enterprise conducts its business so that business activities can be carried out electronically. Abstracting business logic from the application procedures, business rule technology enables the fast development of applications that can be rapidly modified. Potentially e-business system based on business rules can explain themselves. The business rule explanation system of this thesis is a prototype system that provides a user with the justifications of the conclusions derived by a business rule system. The justifications are given in the form of the 'explanation tree' that provides 'how' dialogues to answer how a conclusion is derived, 'why not' dialogues to identify the missing criteria for achieving a goal and 'what if' dialogues to find out a complete set of preconditions that will be necessary to lead to the grant of a request. For e-business systems that deliver services over the Internet, a distributed architecture is required because a business activity sometimes needs to involve different partners under different contexts. The prototype is thus built upon the emerging Web Services standards. The next part of the thesis describes how business rules and Web Services technology work together to deliver a loosely coupled, distributed business rule system.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".