Data Mining from NoSQL Document-Append Style Storages
Bibliographic record
Abstract
The modern data economy, which has been described as "Big Data", has changed the status quo on digital content creation and storage. While data storage has followed the schema-dictated approach for decades, the recent nature of digital content, which is widely unstructured, creates the need to adopt different storage techniques. Thus, the NoSQL database systems have been proposed to accommodate most of the content being generated today. One of such NoSQL databases that have received significant enterprise adoption is the document-append style storage. The emerging concern and challenge however is that, research and tools that can aid data mining processes from such NoSQL databases is generally lacking. Even though document-append style storages allow data accessibility as Web services and over URL/I, building a corresponding data mining tool deviates from the underlying techniques governing web crawlers. Also, existing data mining tools that have been designed for schema-based storages (e.g., RDBMS) are misfits. Hence, our goal in this work is to design a unique data analytics tool that enables knowledge discovery through information retrieval from document-append style storage. The tool is algorithmically built on the inference-based Apriori, which aids us to achieve optimization of the search duration. Preliminary test results of the proposed tool also show high accuracy in comparison to other approaches that were previously proposed.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".