From relations to multi-dimensional maps: a SQL-to-HBase transformation methodology
Bibliographic record
Abstract
In this paper, we describe a methodology for migrating applications relying on relational databases to HBase backends. Our methodology includes (a) a SQL-to-HBASE data-schema migration step, and (b) a transformation of the application SQL queries to equivalent sequences of HBase API calls. Our data-schema migration method relies on a set of HBase-organization guidelines to drive a four-step data-schema transformation process. Some of these guidelines are query-agnostic: we defined them based on related literature regarding the desired properties of the HBase organization. Other guidelines are query-aware: we formulated them to incorporate data-access paths, extracted from query logs, in order to improve the quality of the transformation and the eventual access efficiency of the HBase repository. Our transformation method maintains a mapping between source and target schema that is used to create sequences of HBase API calls, equivalent to SQL queries in the relational database. We illustrate and validate our method with a case study and a comprehensive performance evaluation.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".