Performance considerations in relational and hierarchical data base management systems
Bibliographic record
Abstract
This paper will examine two data base management systems; IMS, an example of a hierarchical data base management system and System R, a relational system. Each system will be described in general terms followed by a discussion of their relative performance. A claim is made that IMS, because of its structure and the procedural nature of its language can be more efficient than System R. Some examples are given to illustrate this claim. There is an increasing need today for readily available information; thus more and more information is being stored in disk files to facilitate rapid retrieval. A relational approach to data base management permits a high-level non-procedural interface for the user, which is important if more people require access to information in a flexible fashion and can not afford or wait for traditional application development. If however a relational data base management system is not as efficient as hierarchical or network systems then new retrieval methods must be found. Associative processors are introduced as a possible solution to this problem and three examples are discussed. RAP, a relational associative processor developed at the University of Toronto, has been used in a performance study to demonstrate the dramatic performance improvements offered by associative processors. This is also discussed.
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".