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Mind the Gap: Large-Scale Frequent Sequence Mining

Summary: MG-FSM enables scalable frequent sequence mining on MapReduce with gap constraints to prune outputs. W-equivalency partitioning enables independent mining with existing algorithms; part-size optimizations cut costs, outperforming prior text mining. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
4694
Venue
SIGMOD
Year
2013
Pagerank
6.5583327e-05
Overall Rank
3,989 | 72.26%
DOI
-

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank Citing Paper Year Venue Pagerank
4,803 A System for Management and Analysis of Preference Data 2014 VLDB 5.9107061e-05
6,111 Why Big Data Industrial Systems Need Rules and What We Can Do About It 2015 SIGMOD 5.2049579e-05
7,597 Oracle Workload Intelligence 2015 SIGMOD 4.7007801e-05
11,903 LASH: Large-Scale Sequence Mining with Hierarchies 2015 SIGMOD 4.1945683e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 1 of 1 cited papers.

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

Rank Cited Paper Year Venue Pagerank
13 Mining Association Rules between Sets of Items in Large Databases 1993 SIGMOD 0.0010864752
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