Bibliographic record
Abstract
Nowadays, there is a big increase in the usage of data analytic applications and services because of the growth in the data produced from many different sources. The QoS properties such as response time, reliability and latency of these services are important factors to decide which services to select. As we know, the energy consumption is becoming a big issue as a result of IT expansion. Therefore, establishing a QoS-based web service selection approach that considers energy consumption as one of the essential QoS properties represents a significant step towards selecting the greener web service. This paper presents an experimental study of energy consumption and latency behavior of data mining algorithms running as web services. Our study shows that, there is a strong relation between the dataset properties such as dataset size, number of attributes, data type, and QoS attributes energy consumption and latency. Based on the findings from our study, a prediction system is built which can be used to predict the energy consumption and latency values for data mining web services on a given dataset, and then these services can be ranked according to their predicted energy and latency values. Experimental results show the effectiveness of our prediction and service selection system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".