A Framework for supporting DBMS-like indexes in the cloud
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To support "Database as a service" (DaaS) in the cloud, the database system is expected to provide similar functionalities as in centralized DBMS such as efficient processing of ad hoc queries. The system must therefore support DBMS-like indexes, possibly a few indexes for each table to provide fast location of data distributed over the network. In such a distributed environment, the indexes have to be distributed over the network to achieve scalability and reliability. Each cluster node maintains a subset of the index data. As in conventional DBMS, indexes incur maintenance overhead and the problem is more complex in the distributed environment since the data are typically partitioned and distributed based on a subset of attributes. Further, the distribution of indexes is not straight forward, and there is therefore always the question of scalability, in terms of data volume, network size, and number of indexes. In this paper, we examine the problem of providing DBMS-like indexing mechanisms in cloud DaaS, and propose an extensible, but simple and efficient indexing framework that enables users to define their own indexes without knowing the structure of the underlying network. It is also designed to ensure the efficiency of hopping between cluster nodes during index traversal, and reduce the maintenance cost of indexes. We implement three common indexes, namely distributed hash indexes, distributed B + -tree-like indexes and distributed multi-dimensional indexes, to demonstrate the usability and effectiveness of the framework. We conduct experiments on Amazon EC2 and an in-house cluster to verify the efficiency and scalability of the framework.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 it