Terms analytics service for CouchDB: a document-based NoSQL
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
The reality that the scientific, industry and research communities have to deal with is the potential of ‘Big Data’. The high-dimensional data (in digitised format) at our disposal can create opportunities such as discovery of new knowledge, creation of new online communities, and improvement on product and services delivery. The challenge however is that there are only few research, architectural designs and tools that can aid data mining processes from NoSQL databases. By focusing on terms and topic mining, this work proposes a data analytics framework that enables knowledge discovery through information retrieval and filtering from document-based NoSQL (specifically, CouchDB). The tool is algorithmically built and tested based on two methodologies namely: the inference-based apriori and the Baum-Welch algorithm. Preliminary test results of the proposed tool are also discussed based on the accuracy of each proposed algorithm where the inference-based apriori model performs better.
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 | 0.001 |
| 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 | 0.002 |
| Open science | 0.004 | 0.001 |
| 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