MEET DB2
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
Commercial databases compete for market share, which is composed of not only net-new sales to those purchasing a database for the first time, but also competitive "win-backs" and migrations. Database migration, or the act of moving both application code and its underlying database platform from one database to another, presents a serious administrative and application development challenge fraught with large manual costs. Migration is typically a high cost effort due to incompatibilities between database platforms. Incompatibilities are caused most often by product specific extensions to language support, procedural logic, DDL, and administrative interfaces. The migration evaluation is the first step in any competitive database migration process. Historically this has been a manual process, with the high costs and subjective results. This has led us to reexamine traditional practices and explore an automatic, innovative solution. We have designed and implemented the Migration Evaluation and Enablement Tool for DB2 for Linux Unix and Windows, or MEET DB2, a tool for automatically evaluating database migration projects. Encapsulated in a simple one-click interface, MEET DB2 is able to provide detailed evaluation of migration complexity based on its deep analysis on the source database. In this paper, we present MEET DB2, and discuss many aspects of our design, and report measurements from real-world use cases. In particular, we show a novel way to use XML and XQuery in this domain for better extensibility and interoperability. We have evaluated MEET DB2 on 18 source code samples, covering nearly 1 million lines of code. The utility has provided benefits in several dimensions including: dramatically reduced time for evaluation, consistency, improved accuracy over human analysis, improved reporting, reduced skill requirements for migration analysis, and clear analytics for product planning.
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 | 0.000 |
| Open science | 0.001 | 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