Service Selection Based on Customer Preferences of Non-Functional Attributes
Bibliographic record
Abstract
With service-oriented systems driving the economies around the world there has been an exponential rise in the number and choices of available services. As a result of this, for most tasks there are a large number of services that can adequately cater to the requirements of the customers. Choosing the service that best conforms to the requirements from the set of functionally equivalent services is non-trivial. Research in the past has utilized the non-functional attributes of such services to select the best service. These efforts however make the assumption that the services with the best non-functional attributes are the ones that most closely conform to the requirements of the customer. This is not always true since the customer may sometimes prefer to settle for a slightly “inferior” service owing to price constraints. In this chapter, we apply the Mid-level Splitting technique to better assess the requirements of the customer and make a more judicious service selection. Furthermore, we also address the issue of assignment of weights to the various non-functional attributes of the services. These weights are reflective of the emphasis that the concerned customer wants to put on the various non-functional attributes of the service. These weights are normally assigned based on the intuition of certain expert personnel and are prone to human error and incorrect judgment. We utilize the Hypothetical Equivalents and Inequivalents technique to more systematically assign weights to the services based on customer preferences. The techniques are demonstrated with a real world example.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".