Enabling an Enhanced Data-as-a-Service Ecosystem
Bibliographic record
Abstract
The sharing of large and interesting Big Data in cloud environments can be achieved using data-as-a-service, where a provider offers data to interested users. In enhanced data-as-a-service, the data provider also supplies compute infrastructure, allowing users to run analytics tasks local to the data and reducing the (expensive and slow) transmission of data over networks. This paper describes a services-based ecosystem that allows providers to precisely share portions of their data with users, using a model where users submit MapReduce jobs that run on the provider's Hadoop infrastructure. Providers are given mechanisms to filter, segment, and/or transform data before it reaches the user's task. The ecosystem also allows for intermediaries who offer value-added filtrations, segmentations, or transformations of the data (for example, pre-filtering a dataset to only include high-income users). We describe the RESTful services required to enable this ecosystem, introduce a prototype to demonstrate the concept, and present experiments using this ecosystem to both provide and analyze different segments of a single large data set.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".