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DataGarage: Warehousing Massive Performance Data on Commodity Servers

Summary: DataGarage warehouses years of performance telemetry from tens of thousands of servers on commodity hardware. Its hybrid DBMS/filesystem/MapReduce architecture addresses the scale and complexity that defeat conventional database and filesystem solutions. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
10219
Venue
VLDB
Year
2010
Pagerank
5.2941406e-05
Overall Rank
9,274 | 36.38%
DOI
10.14778/1920841.1921022

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{loboz_vldb10,
        title = {{DataGarage: Warehousing Massive Performance Data on Commodity Servers}},
        author = {Loboz, Charles and Smyl, Slawek and Nath, Suman},
        journal = {PVLDB},
        series = {{VLDB} '10},
        volume = {3},
        number = {2},
        pages = {1447--1458},
        doi = {10.14778/1920841.1921022},
        url = {https://doi.org/10.14778/1920841.1921022},
        year = {2010}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
1,764 Fast Approximate Correlation for Massive Time-series Data 2010 SIGMOD 9.8120315e-05
4,234 GRAIL: Efficient Time-Series Representation Learning 2019 VLDB 6.8171563e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 7 of 7 cited papers.

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

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