Recovery And Enhancement Of System Patterns Infoschemata And Infomaps
Bibliographic record
Abstract
Evolving systems demand technology transfer. Knowledge is the most important ingredient in technology transfer. However, the creation of knowledge is a complex, slow and expensive process. Recovery and enhancement of knowledge from existing systems for integration into evolving systems is the logical and more attractive approach. Most of system research and development generates complex concepts. A need for the development of schemata in a multidimensional environment is more evident now than ever before. Team work could be converted from a single to a multi-tasking environment and enable developers to perform tasks concurrently. This paper presents the underlying principles for the recovery and development of reusable system patterns. The proposed approach supports crossfunctional development teams, multidimensional modeling and concurrent engineering paradigms. The evolving model of recovering and analyzing existing patterns is described. This model attempts to minimize the conceptual complexity of systems by identifying and abstracting schemata. The analysis performed on the original concepts and methodologies enables the discovery and integration of simple schemata into a library of reusable patterns. The application of a knowledge recovery process to object-oriented methodologies, CASE tools and development of a patterns library is also presented.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".