Business policy modeling and enforcement in databases
Bibliographic record
Abstract
Database systems are the central information repositories for businesses and are subject to a wide array of policies, rules and requirements. The spectrum of business level constraints implemented within database systems has expanded from classical access control to include auditing, usage control, privacy management, and records retention. The lack of a systematic mechanism of integrating and reasoning about such a diverse set of policies manifested as database level constraints makes corporate policy management a chaotic process. In this paper we propose a general purpose policy modeling and constraint management framework that can integrate numerous aspects of business level requirements within database systems. Our proposed solution relies on a finite state modeling language for business level policies, in which users can declaratively express rules related to the normal workflow of a business process as well as specifying any undesirable states of business objects contained in a database system. The proposed system is then able to translate these policies into low level temporal integrity constraints that prevent policy violations and ensure that business objects and artifacts follow their mandated lifecycles. A formal layer for reasoning allows policy makers to discover unenforceable and conflicting policies, providing the basis to guarantee compliance for a wide array of rules that may need to be enforced on complex business objects stored in relational database systems.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".