A Survey of Natural Language Processing Implementation for Data Query Systems
Bibliographic record
Abstract
With increasing complexity and volume of collected data continuing to rise, it is becoming ever more important to develop systems with high interactability. Businesses with an interest in big data continue to seek solutions that limit cost while providing effective, simplified solutions to current issues in data retrieval. Combined analysis and application of a multi-factorial system will likely lead to promising results in ease of reporting of complex data by nontechnical end users. This survey is focused on natural language processing (NLP) implementations for data query systems, especially related to massive data sets (1TB+) in OLTP databases, OLAP databases, and data warehouses. We are seeking the most up-to-date and effective uses of NLP for Speech-to-SQL and Text-to-SQL generation, and the most recent advancements in data warehousing to optimize ELT efficiency and data retrieval, focusing on the highest performing code implementations on the Spider and WikiSQL datasets. Many models, including sequence-to-sequence (seq2seq), sequence-to-SQL (Seq2SQL), and fuzzy semantic to SQL (F-Semtosql), among others, are briefly described and compared. As well, recent advancements in data warehousing technology like multi-disk buffering in the ELT process and hybrid multi-dimensional and relational OLAP databases (HOLAPs) are discussed. The learning gathered here is applied to fill a gap in the current industrial knowledge base in service of increased efficiency in data access, retrieval, and reporting in a customer-facing environment.
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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".