Auto-Grouping Emails For Faster E-Discovery

Summary: Groups emails for e-discovery by near-duplicate syntax, extracted semantic concepts, and thread structure, enabling coherent batch review. Enron experiments show substantial review-time savings with high grouping precision/recall; techniques were productized in IBM eDiscovery Analyzer. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID: 10382
Venue: VLDB
Year: 2011
Pagerank: 5.093636e-05
Overall Rank: 12,388 | 15.01%
DOI: 10.14778/3402755.3402762

Incoming Non-self Citations Over Time

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Authors

1. Sachindra Joshi (IBM)
2. Danish Contractor (IBM)
3. Kenney Ng (IBM)
4. Prasad M Deshpande (IBM)
5. Thomas Hampp (IBM)

BibTeX Citation

@article{joshi_vldb11,
        title = {{Auto-Grouping Emails For Faster E-Discovery}},
        author = {Joshi, Sachindra and Contractor, Danish and Ng, Kenney and Deshpande, Prasad M and Hampp, Thomas},
        journal = {PVLDB},
        series = {{VLDB} '11},
        volume = {4},
        number = {12},
        pages = {1284--1295},
        doi = {10.14778/3402755.3402762},
        url = {https://doi.org/10.14778/3402755.3402762},
        year = {2011}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

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
1,284	Copy Detection Mechanisms for Digital Documents	1995	SIGMOD	0.00011332822

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