Business Intelligence and Data Warehouse in Agrarian Sector: A Bibliometric Study
Bibliographic record
Abstract
Business Intelligence with Data Warehouse technologies are known in the literature as solutions that allow access to business data dynamically and analytical operations on them. Scientific literature lacks works that investigate the current use of these technologies in the agrarian sector, at the international level in the last 10 years. This work presents a bibliometric analysis, which was done through the ProKnow-C methodology, of the application of Business Intelligence and Data Warehouse technologies in the agrarian sector. The objective is to investigate the dissemination of such technologies in this sector in national and international scale. The main findings were the following: number of papers in last years are increasing. Majority of papers were found in the journal named Computers and Eletronics in Agriculture, with a great number of colaborations between authors of France. Few colaborations between authors from different countries were found. Sandro Bimonte was the most cited author. France and India highlight in researches approaching Data Warehouse and Business Intelligence usage in agrarian sciences. The majority of references from Bibliographic Portfolio were from 2001-2010. 66% of papers use some open source technology. Star schema is the most used modelling technique and the use of Unified Modeling Language by authors of France in agricultural Data Warehouse modelling is encouraged. The main limitations were the impossibility of free access in some databases, absence of research on proprietary solutions of technology market in the rural sector and few number of keyword searches.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.172 | 0.252 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".