Design and implementation of a framework for provisioning algorithms as a service
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Designing, implementing and executing algorithms have become a relevant and important element in various fields. Public users and data researchers are interested in analysing and interpreting data with shorter execution time and higher performance. Cloud computing is an environment that provides scalable and high-end virtual resources to achieve high quality services. This paper presents the design, implementation and evaluation of a framework for provisioning algorithms as a service in the cloud. This framework introduces solutions to help clients overcome different concerns and difficulties, such as looking for an appropriate algorithm, understanding algorithm source code, installing and configuring specific libraries, and achieving high algorithmic performance. The framework provides clients the possibility to discover available algorithms and/or deploy new algorithms over multiple scalable platforms. It also allows clients to analyse data, compare results, and measure algorithm's performance. A prototype implementation of the framework has been developed to demonstrate the feasibility of the solution. Evaluating results demonstrate that providing multiple scalability models and high-end web servers will improve algorithm performance and achieve availability and reliability using the framework.
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.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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 it