DeepTrack: Monitoring and Exploring Spatio-Temporal Data - A Case of Tracking COVID-19 -
Summary: DeepTrack interactively monitors COVID-19 by integrating heterogeneous structured and unstructured spatio-temporal sources. Its distinctive feature is generating data-driven, dynamic chart recommendations that surface changing “interesting stories,” alongside static visualizations and ad-hoc modules. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Yuyu Luo (Tsinghua University)
- 2. Wenbo Li (Tsinghua University)
- 3. Tianyu Zhao (Tsinghua University)
- 4. Xiang Yu (Tsinghua University)
- 5. Lixi Zhang (Tsinghua University)
- 6. Guoliang Li (Tsinghua University)
- 7. Nan Tang (Hamad Bin Khalifa University; Qatar Computing Research Institute)
BibTeX Citation
@article{luo_vldb20,
title = {{DeepTrack: Monitoring and Exploring Spatio-Temporal Data - A Case of Tracking COVID-19 -}},
author = {Luo, Yuyu and Li, Wenbo and Zhao, Tianyu and Yu, Xiang and Zhang, Lixi and Li, Guoliang and Tang, Nan},
journal = {PVLDB},
series = {{VLDB} '20},
volume = {13},
number = {12},
pages = {2841--2844},
doi = {10.14778/3415478.3415489},
url = {https://doi.org/10.14778/3415478.3415489},
year = {2020}
}Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,809 | Synthesizing Natural Language to Visualization (NL2VIS) Benchmarks from NL2SQL Benchmarks | 2021 | SIGMOD | 6.4984309e-05 |
| 7,199 | Learned Data-aware Image Representations of Line Charts for Similarity Search | 2023 | SIGMOD | 5.6759927e-05 |
| 9,976 | Sevi: Speech-to-Visualization through Neural Machine Translation | 2022 | SIGMOD | 5.1845938e-05 |
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