DBScholar

Back to papers

ST4ML: Machine Learning Oriented Spatio-Temporal Data Processing at Scale

Summary: ST4ML enables scalable ML-focused spatio-temporal analytics via a 3-stage pipeline (selection-conversion-extraction) on Spark. First ML-centric ST analytics; ST4ML yields up to 10x speedups on real data and is open-source at github.com/Panrong/st4ml. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6652
Venue
SIGMOD
Year
2023
Pagerank
5.093636e-05
Overall Rank
11,392 | 21.85%
DOI
10.1145/3588941

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

BibTeX Citation

@inproceedings{liu_sigmod23,
        title = {{ST4ML: Machine Learning Oriented Spatio-Temporal Data Processing at Scale}},
        author = {Liu, Kaiqi and Tong, Panrong and Li, Mo and Wu, Yue and Huang, Jianqiang},
        series = {{SIGMOD} '23},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3588941},
        url = {https://dl.acm.org/doi/10.1145/3588941},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank Citing Paper Year Venue Pagerank
Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 14 of 14 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Previous Page 1 / 1 Next

Semantically Similar Papers