Unstructured data mining: use case for CouchDB
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
'Big data' has changed the status quo on digital content creation, storage and management. While data hoarding over the years has followed the structured-style storage approach, the recent nature of digital content, which is widely unstructured, creates the need to adopt different storage techniques. The NoSQL database systems are therefore 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 problem however is that, research and tools that can aid data mining tasks 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 data analytics tool that enables knowledge discovery through information retrieval (i.e., terms) from document-append style storage. Three algorithms for terms extraction are tested which are: the inference-based apriori with a Bayesian component, the hidden Markov model, and the Bernoulli process. Overall, the paper proves the accuracy and speed of each algorithm.
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 | 0.002 |
| 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 | 0.001 | 0.003 |
| Open science | 0.011 | 0.002 |
| 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