Lessons Learned from Integrating Batch and Stream Processing using IoT Data
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 unbounded data streams generated by IoT sensors/devices are posing many technical challenges and requires a one-size-fits-all solution to cope with the massive amount and the high speed of the incoming IoT data arriving simultaneously. In this study, we try to integrate batch and stream processing in a unique system as a premise to handle Volume and Velocity aspects of IoT data simultaneously. In order to handle current, outdated, and historical IoT data streams, we built a cloud architecture to execute the analytical workflows using both batch and stream processing in a synergetic manner. A smart parking case study is used to evaluate the architecture and two experiments are implemented to demonstrate a web application for predicting parking spot availability. Herein, we learned our lessons that there are several hindrances to finding a middle ground where current, outdated and historical IoT data streams can be used in a strategic way.
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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 | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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