A Parallel Processing Technique for Filtering and Storing User Specified 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
Users are often interested in a specific type of data (user-preferred data) from a large-volume dataset. An efficient system that only stores user-preferred data from the large dataset can reduce the search latency, which allows the users to search for relevant information in a timely manner. The motivation behind this thesis is to devise a technique that filters a large dataset and stores only the filtered data, thereby saving storage space for the user. Running the filtering operation can be CPU-intensive, which can lead to high latency in extracting preferred data from the dataset. To solve this problem, the technique employs parallel processing and machine learning. A proof-of-concept prototype for this technique has been built on Apache Spark. The performance of the prototype subjected to synthetic datasets is analyzed. The analysis of experimental results shows the viability of this technique and provides insights into the system behavior and performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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