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A New Approach for Processing Ranked Subsequence Matching Based on Ranked Union

Summary: Models ranked subsequence matching as ranked union; introduces MSEQ and MSEQ-distance for lower-bounds and pruning. Cost-aware density-based PQ scheduling eliminates HLMJ overhead; yields up to 2–3 orders of magnitude speedups over HLMJ and PSM on real data. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
4404
Venue
SIGMOD
Year
2011
Pagerank
4.6009403e-05
Overall Rank
8,035 | 44.11%
DOI
-

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Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
6,983 A Generic Framework for Efficient and Effective Subsequence Retrieval 2012 VLDB 4.8732757e-05
11,921 SMiLer: A Semi-Lazy Time Series Prediction System for Sensors 2015 SIGMOD 4.1945683e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 13 of 13 cited papers.

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

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