Automating Common Data Integration for Improved Data-Driven Decision-Support System in Industrial Construction
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
To achieve meaningful results, data-driven decision-support systems in construction require the integration of fragmented data from multiple standalone databases. In practice, a manual brute-force approach is often the only available means of integrating structured, yet semantically-ambiguous, construction data. Two common data integration challenges include the identification of (1) key strings (i.e., product identification) partially shared between two data sources; and (2) relationships (overlap, included, or outside) between two 3D object lists based on coordinates. This research has developed a framework that includes two generic solutions to the identified semantic mapping challenges. The proposed framework automatically integrates fragmented and incompatible data (exhibiting similar semantic mapping challenges) from various sources into a tidy format for input into a diverse range of industrial construction applications. Verification and functionality of the framework were confirmed using both artificial data and a real case study of a large oil-and-gas project. The ability of the proposed data integration functions and framework to automate otherwise manual data integration processes was demonstrated. Results of this study are expected to enhance real-time information flow, improve data quality, and promote the use of fragmented data for critical decision support in practice.
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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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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