BASIC: An alternative to BASE for large-scale data management system
Bibliographic record
Abstract
Big data applications demand and consequently lead to developments of large-scale data management systems, which provide high scalability by partitioning data across multiple servers. Since conventional transactional access is quite expensive, many real world large-scale distributed systems eschew transactional functionality and adopt semantics of atomic multi-partition operations. Accordingly, BASE, a consistency model weaker than ACID, is commonly used to guarantee availability. In this work, we identify a new consistency model-BASIC (Basic Availability, Scalability, Instant Consistency) that matches the requirements where extra efforts are not needed to manipulate inconsistent soft states. We present a timestamp-based formula protocol for BASIC that can enforce Instant Consistency while achieving linear scalability (via logical formula caching, dynamic timestamp ordering) and achieve Basic Availability in the presence of partial failure and network partition (via partition independence, genuine atomic commit). Our extensive experimental results verify the scalability of BASIC and demonstrate that the limited overhead induced by BASIC pays a reasonable price for keeping all soft states consistent.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".