Efficient SQL querying on embedded devices using pre-compilation
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
Microprocessors and embedded devices are used for data collection and analysis applications in infrastructure and en- vironmental monitoring, medical technology, wearable com- puting, and sensor network and mobile systems. Such appli- cations demand low energy solutions without using too much of a device's extremely limited RAM (1KB-100KB) and code space. Previously available database software for embedded devices and sensor networks relied heavily on data trans- mission across networks for centralized data processing. Re- cently, relational database systems for resource-constrained devices have been developed to execute queries on a per- device basis, which saves network transmission overhead. This work extends the applicability of such systems by lower- ing the code space and execution time requirements further through serializing queries at application build time and re- moving the query translation component from the device. By eliminating the need for complex query translation sys- tems on device, our technique can reduce ROM usage by as much as 50% while improving memory utilization. Our ex- periments demonstrate that pre-compiling can reduce query initialization times by 90% compared to typical parsing tech- niques. This translates to a further savings of up to 50% in on-device total execution times. The technique developed is applicable to a wide variety of embedded systems and
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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