MixApart: decoupled analytics for shared storage systems
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
Distributed file systems built for data analytics and enterprise storage systems have very different functionality requirements. For this reason, enabling analytics on enterprise data commonly introduces a separate analytics storage silo. This generates additional costs, and inefficiencies in data management, e.g., whenever data needs to be archived, copied, or migrated across silos.MixApart uses an integrated data caching and scheduling solution to allow MapReduce computations to analyze data stored on enterprise storage systems. The front-end caching layer enables the local storage performance required by data analytics. The shared storage back-end simplifies data management.We evaluate MixApart using a 100-core Amazon EC2 cluster with micro-benchmarks and production workload traces. Our evaluation shows that MixApart provides (i) up to 28% faster performance than the traditional ingest-then-compute workflows used in enterprise IT analytics, and (ii) comparable performance to an ideal Hadoop setup without data ingest, at similar cluster sizes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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