TD-Join: Leveraging Temporal Dependencies in Time Series Joins

Summary: TD-Join extends MP-based subsequence joins with Allen's Interval Algebra to capture temporal relations (before, overlaps, meets, equal) between matching subsequences. It enables efficient discovery, querying, and interpretation of temporal dependencies, yielding semantic meaning beyond numerical similarity. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 7248
Venue: SIGMOD
Year: 2025
Pagerank: 5.093636e-05
Overall Rank: 10,738 | 26.33%
DOI: 10.1145/3722212.3725137

Incoming Non-self Citations Over Time

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Authors

1. Gianluca Rossi (Claude Bernard Lyon 1 University)
2. Riccardo Tommasini (Institut National des Sciences Appliquees de Lyon)
3. Angela Bonifati (Claude Bernard Lyon 1 University)

BibTeX Citation

@inproceedings{rossi_sigmod25,
        title = {{TD-Join: Leveraging Temporal Dependencies in Time Series Joins}},
        author = {Rossi, Gianluca and Tommasini, Riccardo and Bonifati, Angela},
        series = {{SIGMOD} '25},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3722212.3725137},
        url = {https://dl.acm.org/doi/10.1145/3722212.3725137},
        year = {2025}
}

Incoming Citations (Sorted by Pagerank)

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Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 2 of 2 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
1,805	SAND: Streaming Subsequence Anomaly Detection	2021	VLDB	9.7116108e-05
6,959	Time Series Data Mining: A Unifying View	2023	VLDB	5.7303405e-05

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