Java and JDBC: tools supporting data-centric business application development
Bibliographic record
Abstract
Software porting, maintenance, distribution and installation form the bulk of application deployment costs. These high costs have been substantially exacerbated by the move to client server computing. No longer can an application be installed on one system for use by the entire user community. Now it must be installed on all desktops in the enterprise and, at most companies, this will be mean that the software must be ported to and tested on Windows 3.1, Macintosh, Windows95, Windows NT, and often a variety of UNIX systems. The trend towards making business data available to both internal and external users across intranets and the Internet transforms an expensive problem into a completely intractable one. To further complicate the matter, there is an emerging trend in many application domains to move from 2 dimensional windowed user interfaces to high information bandwidth 3 dimensional data visualization and virtual reality systems. These applications are either very difficult or impossible to write using standard GUI builders, generators, or the current breed of portable GUI class libraries. We propose a new architecture for data visualization, discovery, and delivery and argue that the existing development model, and the tools supporting that model, are unaffordable and don't scale to Internet scope delivery numbers.
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.
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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.027 |
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".