From relations to multi-dimensional maps: a SQL-to-HBase transformation methodology
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
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.
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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 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