Mining large‐scale high utility patterns in vehicular ad hoc network environments
Bibliographic record
Abstract
Abstract One well‐known type of mobile ad hoc network is known as a vehicular ad hoc network (VANET). The functions of such a network are integrated into a new generation of wireless networks for vehicles, which has established a robust self‐organizing network that exists between roadside units and mobile vehicles. In this article, we research the comfort applications in VANET with a new proposed algorithm, EHUM, short form for efficient high utility itemset mining, to mine patterns of the more popular Points of Interest (POIs). This algorithm is based on the traditional high‐utility itemset mining (HUIM) algorithm, and we propose a more reasonable pruning strategy. Concurrently, for solving the problem of excessive data volume in VANET, we applied this algorithm to the MapReduce architecture used for improving the feasibility in practical applications. Our in‐depth work in this article culminates with some experimental results that clearly show that our proposed algorithm can perform well to mine the POIs pattern in a big data data set and shows great performance in a Hadoop computing cluster.
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.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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 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".