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: 4756
Venue: SIGMOD
Year: 2013
Pagerank: 6.6373455e-05
Overall Rank: 4,547 | 68.81%
DOI: 10.1145/2463676.2465285

Incoming Non-self Citations Over Time

Authors

1. Iris Miliaraki (Max Planck Institute)
2. Klaus Berberich (Max Planck Institute)
3. Rainer Gemulla (Max Planck Institute)
4. Spyros Zoupanos (Max Planck Institute)

BibTeX Citation

@inproceedings{miliaraki_sigmod13,
        title = {{Mind the Gap: Large-Scale Frequent Sequence Mining}},
        author = {Miliaraki, Iris and Berberich, Klaus and Gemulla, Rainer and Zoupanos, Spyros},
        series = {{SIGMOD} '13},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/2463676.2465285},
        url = {https://dl.acm.org/doi/10.1145/2463676.2465285},
        year = {2013}
}

Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
6,068	Why Big Data Industrial Systems Need Rules and What We Can Do About It	2015	SIGMOD	5.9887211e-05
7,642	A System for Management and Analysis of Preference Data	2014	VLDB	5.5765908e-05
7,740	Oracle Workload Intelligence	2015	SIGMOD	5.5550801e-05
12,102	LASH: Large-Scale Sequence Mining with Hierarchies	2015	SIGMOD	5.093636e-05

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	0.0006567919

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