Reliable Consumption of Web Services in a Mobile-Cloud Ecosystem Using REST
Bibliographic record
Abstract
The evolution of the mobile landscape coupled with the ubiquitous nature of the Internet and the cloud is facilitating the deployment of enterprise and personalized mobile applications. In this research, we proposed a proxy-enabled unification framework that integrates heterogeneous devices with multiple SaaS and IaaS cloud layers in order to support personalized and group file sharing. However, our proposed mobile-cloud ecosystem calls for open research questions which must be answered such as i) how do we synchronize the data across the consumer devices and the multi-IaaS backend?, ii) how do we authenticate the system users?, and iii) how do we push updates in a low-latency fashion? This paper addresses the three questions by proposing the adoption of the REST Web Service as an efficient way to consume the data on the mobile devices. However, we have to deal with the "CAP Theorem" which states that we can only achieve at most two properties at a time out of the following three: data consistency, system/data availability, and partition tolerance. Since partition tolerance is a given in a distributed system, we opt for the availability option by allowing file storage on the consumer devices in both online and offline modes. Further, we propose data consistency within a session that enforces update propagation in a soft-real time. The architecture is evaluated based on latency and scalability using multi consumer devices and employed Drop box and Amazon S3 as the IaaS cloud providers.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".