Bibliographic record
Abstract
While many access control models have been proposed, little work has been done on the efficiency of access control systems. Because the access control sub-system of an Enterprise Content Management (ECM) system may be a bottleneck, we investigate the representation of permissions to improve its efficiency. Observing that there are many browsing-oriented permission request queries, we choose to implement a subject-oriented representation (i.e., maintaining a permission list for each subject). Additionally, we notice that with breadth-first ID numbering we may encounter many contiguous IDs under one object (e.g., folder) . \n \nTo optimize the efficiency taking into account the above two characteristics, this thesis presents a space-efficient data structure specifically tailored for representing permission lists in ECM systems. Besides the space efficiency, checking, granting or revocation of a permission is very fast using our data structure. It also supports fast union of two or more permission lists (determining the effective permissions inherited from users' groups). In addition, our data structure is scalable to support any increase in the number of objects and subjects. \n \nWe evaluate our representation by comparing it against the bitmap based representation and a hash table based representation while using random ID numbering and breadth-first numbering, respectively. Our experimental tests on both synthetic and real-world data show that the hash table outperforms our representation for regular permission queries (i.e., querying permissions on a single object each time) as well as browsing-oriented queries with random ID numbering. However, our tests also show that 1) our representation supports faster browsing-oriented queries with breadth-first ID numbering applied while consuming only half the space when compared to the hash table based representation, and 2) our representation is much more space and time efficient than the bitmap based representation for our application.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".