Visualization and Integration of Databases Using Self-Organizing Map
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
With the growing computer networks, accessible data is becoming increasingly distributed. Understanding and integrating remote and unfamiliar data sources are important data management issues. In this paper, we propose to utilize self-organizing maps (SOM) clustering to aid with the visualization of similar columns, and integration of relational database tables and attributes based on the content. In order to accommodate heterogeneous data types found in relational databases, we extended the TFIDF measure to handle, in addition to text, numerical attribute types for coincident meaning extraction. We present a SOM clustering based visualization algorithm allowing the user to browse the heterogeneously typed database attributes and discover semantically similar clusters. Additionally, we propose a new algorithm Common Item Based Classifier (CIBC) to smoothen the homogeneity of the clusters obtained by SOM. The discovered semantic clusters can significantly aid in manual or automated constructions of data integrity constraints in data cleaning or schema mappings in data integration.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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