Parallel gathering discovery over big trajectory 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
The advances in location-acquisition technologies have generated massive spatio-temporal trajectory data, which represent the mobility of a diversity of moving objects over time, such as people, vehicles, and animals. Discovery of traveling companions on trajectory data has many real-world applications. Most of existing discovery approaches are limited to centralized computing, while these techniques for handling large-scale trajectory data require considerable performance improvement. Parallel computing essentially provides an alternative method for handling this problem. In this work, we first present the design and implementation of both batch and streaming gathering patterns discovery algorithm in a distributed parallel computing fashion. Afterwards, we further propose several optimization techniques for efficient computation. Finally we conduct extensive experiments based on a public dataset to evaluate the efficiency of our approaches and effectiveness of optimizations using Amazon EC2 clusters.
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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 | 0.004 |
| Open science | 0.002 | 0.002 |
| 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