Data Management for High-Throughput Genomics

Summary: Proposes using relational DB (SQL Server) as the primary storage and processing platform for high-throughput sequencing workloads, addressing tens of TB/week of data. Describes a DB schema, unconventional storage tricks, and leveraging SQL/UDFs for in‑database analysis with initial scalability and performance findings. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: 123
Venue: CIDR
Year: 2009
Pagerank: 5.093636e-05
Overall Rank: 12,484 | 14.35%
DOI: -

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

1. Uwe Röhm (University of Sydney)
2. José A. Blakeley (Microsoft)

BibTeX Citation

@inproceedings{rohm_cidr09,
        address = {Amsterdam, Netherlands},
        series = {{CIDR} '09},
        title = {{Data Management for High-Throughput Genomics}},
        booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
        author = {Röhm, Uwe and Blakeley, José A.},
        year = {2009}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
1,118	The POSTGRES Data Model	1987	VLDB	0.00012104924
6,978	Hosting the .NET Runtime in Microsoft SQL Server	2004	SIGMOD	5.7303405e-05
12,560	.NET Database Programmability and Extensibility in Microsoft SQL Server	2008	SIGMOD	5.093636e-05

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