Distributed and parallel high utility sequential pattern mining
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
The problem of mining high utility sequential patterns (HUSP) has been studied recently. Existing solutions are mostly memory-based, which assume that data can fit into the main memory of a computer. However, with advent of big data, such an assumption does not hold any longer. Hence, existing algorithms are not applicable to the big data environments, where data are often distributed and too large to be dealt with by a single machine. In this paper, we propose a new framework for mining HUSPs in big data. A distributed and parallel algorithm called BigHUSP is proposed to discover HUSPs efficiently. At its heart, BigHUSP uses multiple MapReduce-like steps to process data in parallel. We also propose a number of pruning strategies to minimize search space in a distributed environment, and thus decrease computational and communication costs, while still maintaining correctness. Our experiments with real life and large synthetic datasets validate the effectiveness of BigHUSP for mining HUSPs from large sequence datasets.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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