Efficient SQL querying on embedded devices using pre-compilation
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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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".