Parallel Subtrajectory Alignment over Massive-Scale Trajectory Data
Authors: Lisi Chen, Shuo Shang, Shanshan Feng, Panos Kalnis
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments with large trajectory datasets are conducted for evaluating the performance of our proposal. |
| Researcher Affiliation | Academia | 1University of Electronic Science and Technology of China 2Harbin Institute of Technology, Shenzhen, China 3King Abdullah University of Science and Technology, Saudi Arabia |
| Pseudocode | Yes | Algorithm 1: Traj Candidates Gen |
| Open Source Code | No | The paper does not include an explicit statement about releasing its source code or a direct link to a repository for the methodology described. |
| Open Datasets | Yes | For BN, we use taxi trajectory data collected by the T-drive project [Yuan et al., 2013]... For NYN, we use taxi trip data from New York3 |
| Dataset Splits | No | The paper mentions that |
| Hardware Specification | Yes | All algorithms are implemented in Java and run on a server with two Intel Xeon Processors Gold 5120 and 64GB RAM. |
| Software Dependencies | No | The paper only mentions |
| Experiment Setup | Yes | Unless stated otherwise, experiment results are averaged over 100 independent trials using different vs, ve, and O for efficacy and efficiency evaluations. The parameter settings are listed in Table 1. |