Advances in the Smart Data Analytics Framework: Integrating Data Extraction and Automated Reporting
Bibliographic record
Abstract
The efficient fusion of information from different data silos in a process plant is essential for a smart data analytics platform. Researchers have developed a GUI-based framework, namely, AMtool, to integrate alarm data and sensor (process) data complemented with process connectivity information. This state-of-the-art smart data analytics framework integrates advanced techniques for data analysis and visualization, performance evaluation, and alarm configuration. This framework also supports alarm flood analysis along with documentation to aid alarm rationalization, enabling industrial processes to become compliant with industry standards. However, an earlier version of the AMtool faced limitations in meeting the automation and real-time data exchange requirements emphasized by the Industry 4.0 paradigm. In this paper, we discuss the recent advances and improvements to make the tool compliant with Industry 4.0 standards, namely, the data extraction interface and automated reporting feature. The data extraction interface facilitates secure and efficient data collection via SQL databases and OPC UA platforms. Additionally, the report generation is more automated for an efficient and more user-friendly option for the regular generation of alarm analysis reports.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".