Bibliographic record
Abstract
Replicating data across different databases has the potential to provide low response times since data can be accessed locally, high scalability since load can be distributed, and fault-tolerance since the data can be accessed as long as one replica is available. A big challenge of database replication is to handle updates such that the entire replicated database appears as if there is only a single logical copy of the data. The standard correctness criterion for database replication is 1-copy-serializability (1-copy-SE) which guarantees that a replicated database behaves as a non-replicated database with serializability (SE), the highest isolation level for transaction execution. In this thesis, we propose a new criterion, 1-copy-snapshot-isolation (1-copy-SI), due to the popularity of snapshot isolation (SI) over serializability in major database systems. SI allows some non-serializable executions, but it provides better concurrency and can be implemented efficiently. However, current definitions of SI allow for the violation of integrity constraints while commercial implementations of snapshot isolation maintain them. Hence, we define a new isolation level SI+IC which represents the isolation level implemented in current systems. From there, we propose a criterion 1-copy-SI+IC that respects both SI and integrity constraints in a replicated database. As a second step, we develop a replication solution that provides many features. It provides 1-copy-SI+IC. It is implemented in a middleware between clients and original database system, and thus, does not require changes to the source code of the database system. Despite being at the middleware level, it provides concurrency at the record level, and thus, the same concurrency level as the database system itself. Furthermore, it provides a standard database interface, and thus, is transparent to the application. It also offers fault-tolerance. Finally, it includes protocols that are able to handle a wide-area environment. This is achieved by a careful choice of communication patterns that keep communication across the wide area network at a minimum. The approach is implemented within a middleware-based framework that allows for an easy plug-in of replication algorithms. Our solution is carefully evaluated, comparing several design alternatives. Additionally, it is compared against a traditional replication protocol, that is widely implemented in current systems. The evaluation shows that our protocols have very good performance and compare favourably with existing solutions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".