Data-Aware Web Service Recommender System for Energy-Efficient Data Mining Services
Bibliographic record
Abstract
Data analytic and data mining services are widely used these days as a result of the ever-expanding amount of data being produced from various applications. Additionally, the number of publicly available data mining and data analytic services is steadily increasing, which makes it hard for a user to select a proper service among a large number of existing candidate services. Furthermore, the quality of data mining algorithms and services implementing these algorithms can be heavily affected by the input datasets and their properties. Energy consumption is one of such QoS (Quality of Service) property. Reducing the energy consumption resulting from using web services can be beneficial to many sectors. Recommender system (RS) has been successfully used for selecting web services based on user's preferences and experiences on QoS properties. Context-aware recommender system can further improve both the recommendation accuracy and prediction accuracy by incorporating the contextual information in the recommendation process. In the existing related work, there is a limitation of not considering data properties in web service recommendation. Therefore, in this paper, we propose to add data, or more accurately, dataset properties, as a contextual information that can be integrated into the web service recommender system for data mining services. In particular, we use the matrix factorization model to implement our recommender system for recommending energy-efficient web services. Experiment results show the effectiveness of the proposed approach on our collected data. The accuracy of the QoS values prediction has been improved by 61% and the recommendation accuracy is improved by 32%.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".