Frequent and non-frequent pattern detection in big data streams: An experimental simulation in 1 trillion data points
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
Big data streaming analysis nowadays has become one of the most important topic in the list of data analysts since enormous amount of data are produced daily by the numerous smart devices. The analysis of such data is very important and the detection of frequent or even non-frequent patterns can be critical for many aspects of our lives. In the current paper, we propose a new methodology based on our previous work regarding the detection of all repeated patterns in a string in order to analyze a very big data stream with 1 Trillion digits, composed from 1 thousand subsequences of 1 billion digits each one. More specifically, using the novel data structure, LERP Reduced Suffix Array, and the innovative ARPaD algorithm which allows the detection of all repeated patterns in a string we managed to analyze each one of the 1 billion data points, using 10 computers with standard hardware configuration, in 33 minutes which outperforms to the best of our knowledge any other existing methodology, which is equivalent to data point generation every 2 microseconds.
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 | 0.001 | 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 | 0.002 |
| Open science | 0.001 | 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