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Nexus: Correlation Discovery over Collections of Spatio-Temporal Tabular Data

Summary: Nexus aligns heterogeneous spatio-temporal tabular datasets in a large repository to support exploratory correlation discovery as a precursor to causal analysis. Key novelty: robust cross-dataset space/time alignment with missing-data handling plus ranking of “interesting” correlations, validated on Chicago open data and UN datasets. (summarized by gpt-5.4-mini on May 24 2026)

Paper ID
6980
Venue
SIGMOD
Year
2024
Pagerank
5.3904679e-05
Overall Rank
8,658 | 40.60%
DOI
10.1145/3654957

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{gong_sigmod24,
        title = {{Nexus: Correlation Discovery over Collections of Spatio-Temporal Tabular Data}},
        author = {Gong, Yue and Galhotra, Sainyam and Fernandez, Raul Castro},
        series = {{SIGMOD} '24},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3654957},
        url = {https://dl.acm.org/doi/10.1145/3654957},
        year = {2024}
}

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