RSenter: terms mining tool from unstructured data sources
Bibliographic record
Abstract
The emergence of ‘Big Data’ is changing the data storage status quo at the business and corporate level. Previously, relational databases have been employed to accommodate business-related digital records but in today’s data economy, the data is unstructured which puts limitations on relational databases. Thus, NoSQL databases have been proposed to contain the unstructured data which is chiefly schema-less, textual, file-based, and so on. However, the rise of unstructured data and the adoption of NoSQL storages lead to emerging challenges that call for active research. Firstly, existing data mining techniques are designed for schema-based data storages and are inapplicable to NoSQL storages. Secondly, NoSQL storages are from different vendors (or, providers) so require the understanding of multiple APIs to generate queries. These two challenges hinder data extraction for most businesses since information stored can be lost due to inaccessibility. Our ongoing research has therefore proposed a tool called RSenter that aids terms mining from unstructured data storages. Specific to NoSQL storages that are document-oriented, we detail the architectural design, the algorithms, and the benefits that distinguish the tool from other existing frameworks. Significantly, RSenter performs the required mining tasks in real-time which is crucial for business continuity.
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.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
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".