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
Data analytics and enterprise applications have very dif-ferent storage functionality requirements. For this rea-son, enterprise deployments of data analytics are on a separate storage silo. This may generate additional costs and inefficiencies in data management, e.g., whenever data needs to be archived, copied, or migrated across si-los. We introduce MixApart, a scalable data processing framework for shared enterprise storage systems. With MixApart, a single consolidated storage back-end man-ages enterprise data and services all types of workloads, thereby lowering hardware costs and simplifying data management. In addition, MixApart enables the local storage performance required by analytics through an in-tegrated data caching and scheduling solution. Our pre-liminary evaluation shows that MixApart can be 45% faster than the traditional ingest-then-compute workflow used in enterprise IT analytics, while requiring one third of storage capacity when compared to HDFS. 1
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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