Discovering Longest-lasting Correlation in Sequence Databases
Summary: Discovering the longest-lasting highly correlated subsequences in sequence databases without a predefined query length. Introduces a space-constrained index with intra- and inter-object grouping that bounds correlations for subsequences of similar length and offset, enabling a normalized distance metric and scalable evaluation. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Yuhong Li
- 2. Leong Hou U
- 3. Man Lung Yiu
- 4. Zhiguo Gong
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,685 | NLC: Search Correlated Window Pairs on Long Time Series | 2022 | VLDB | 4.4675898e-05 |
| 8,985 | TSM-Bench: Benchmarking Time Series Database Systems for Monitoring Applications | 2023 | VLDB | 4.4156106e-05 |
| 11,215 | Correlation Joins over Time Series Data Streams Utilizing Complementary Dimension Reduction and Transformation | 2023 | SIGMOD | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 12 of 12 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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