Lessons Learned from Integrating Batch and Stream Processing using IoT Data
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 In order to handle current, outdated, and historical IoT data streams, we built a cloud architecture to execute the analytical workflows using both batch and stream processing in a synergetic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 Herein, we learned our lessons that there are several hindrances to finding a middle ground where current, outdated and historical IoT data streams can be used in a strategic way.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.014 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".