Data Lake Lambda Architecture for Smart Grids Big Data Analytics
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
The advances in smart grids are enabling huge amount of data to be aggregated and analyzed for various smart grid applications. However, the traditional smart grid data management systems cannot scale and provide sufficient storage and processing capabilities. To address these challenges, this paper presents a smart grid big data eco-system based on the state-of-the-art Lambda architecture that is capable of performing parallel batch and real-time operations on distributed data. Furthermore, the presented eco-system utilizes a Hadoop Big Data Lake to store various types of smart grid data including smart meter, images, and video data. An implementation of the smart grid big data eco-system on a cloud computing platform is presented. To test the capability of the presented eco-system, real-time visualization and data mining applications were performed on the real smart grid data. The results of those applications on top of the eco-system suggest that it is capable of performing numerous smart grid big data analytics.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.014 | 0.007 |
| 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