A Technique for Extracting User Specified Information from Streaming Data
Bibliographic record
Abstract
Users are often interested in a specific type of data (user-preferred data) from large volumes of data streaming into the system in real-time. An efficient system that only stores user-preferred data from these data can reduce storage space by storing only the user-preferred data and discarding the remaining data. It also reduces the search latency which allows the users to search for relevant information in a timely manner. The motivation behind this research is to devise a technique that filters streaming data and stores only the filtered data for post-processing, thereby saving storage space and reduce search latency. A proof-of-concept prototype for this technique has been built on Apache Spark by leveraging the parallel processing framework for streaming data and its machine learning library. The performance of the prototype subjected to synthetically generated streaming data is analyzed. The analysis of experimental results demonstrates the efficacy of this technique and provides insights into system behavior and performance.
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How this classification was reachedexpand
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 | 0.000 | 0.005 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".